Hybrid Energy System Research Articles

Resilience-centered optimal sizing and scheduling of a building-integrated PV-based energy system with hybrid adiabatic-compressed air energy storage and battery systems

This study investigates the economic and resilience co-optimization of a decentralized hybrid energy system (HES) within scenarios involving limited energy sources and a hybrid energy storage solution. The HES is comprised of a building-integrated Photovoltaic (PV) system incorporating an adiabatic compressed air energy storage (A-CAES) and batteries, with the main grid, serving as a backup. A two-stage sizing-scheduling model is proposed to optimize the configuration, minimize lifetime costs, and enhance both long and short-term resiliency while achieving an optimal schedule. The charging/discharging transition time of A-CAES is also adopted in the operational model. The results indicate a substantial annual resiliency improvement of approximately 41.1 %, with optimal sizing and energy storage integration. Additionally, they highlight the cost-effectiveness of the PV/A-CAES system while emphasizing the higher self-sufficiency achieved by the PV/A-CAES/battery system, attaining an electrical load management ratio of 47.3 % and a PV self-consumption rate of 96 %, a 6 % improvement over HESs with individual A-CAES. Furthermore, it is presented that under an optimal operational condition, even with the highest PV power availability during grid interruptions, HES relying solely on individual A-CAES could meet 94 % of the load demand. Integrating a fast-response battery enhances this resiliency to 100 %, effectively reducing PV-power curtailment, particularly during grid interruptions and A-CAES transitions.

Data-driven seasonal scenario generation-based static operation of hybrid energy systems

Integrating intermittent wind and solar energy into hybrid energy system has introduced significant operational uncertainties. This paper develops a static operation model incorporating biomass gasification-based combined heat and power as a coupling center based on conceptual utility grid-connected real data in Sacramento, California. This study involves generating typical scenarios with seasonal characteristics and demand correlations to capture key input parameters accurately. Subsequently, the Newton-Raphson method was developed to calculate the energy flow within these scenarios. Simulation results demonstrate the proposed method achieves over 70 % construction accuracy across different seasonal scenarios. The economic results that the winter electricity loads increased by 44.8 % compared to summer, with corresponding rises in gas and heat loads by 360.6 % and 372.3 %, respectively, resulting in the hybrid energy system economic cost increase of 58.9 %. These results confirm the model's robustness in effectively managing intermittent energy sources and addressing the economic impacts of seasonal demand variations.

Electricity and hydrogen fuel generation based on wind, solar energies and alkaline fuel cell: A hybrid IWGAN–AVOA approach

Addressing the pressing need for sustainable energy solutions, this paper proposes a novel hybrid energy system integrating wind, alkaline fuel cells and solar energies. This paper introduces a pioneering hybrid energy system designed to produce electricity and hydrogen fuel through the integration of wind, solar energies, and an alkaline fuel cell. The proposed approach, coined as IWGAN–AVOA technique, synergizes the improved Wasserstein generative adversarial network (IWGAN) and the African vultures optimization algorithm (AVOA). The primary aim is to delineate a distinctive energy cycle reliant on renewable sources, featuring a Stirling engine, electrolyzer, alkaline fuel cell, wind turbine, and solar photovoltaic system, with the objective of generating hydrogen fuel and energy. The AVOA enhances fuel cell capabilities, simulates optimal system component sizes, and rectifies erroneous configurations based on geographical specifics. By validating the efficacy of IWGAN, the study achieves optimal configurations for device capacities and enhances power flow efficiency. Comparative analyses reveals that the IWGAN–AVOA approach surpasses existing techniques, like particle swarm optimization (PSO), wild horse optimizer (WHO), and heap based optimizer (HBO), with a remarkable efficiency rate of 98%, outperforming current methods. The last figure illustrates the effectiveness comparison, showcasing efficiencies of 62%, 79%, and 85% for HBO, PSO, and WHO, respectively, against the superior 98% efficiency achieved by the IWGAN–AVOA approach, affirming its superiority in energy system optimization.

Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions.

The Advancement of Artificial Intelligence's Application in Hybrid Solar and Wind Power Plant Optimization: A Study of the Literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.

Mixed-integer disciplined convex programming approach applied to the optimal energy supply of near-zero energy buildings

Energy needs in the buildings sector accounts for 40 % of global energy demand. Therefore, the implementation of several renewable energy sources is necessary to reduce this demand. The design stage of a decentralized generation project requires quantifying the power to be installed and the energy forecast for each source throughout the useful life of the building. This study develops a novel optimization algorithm for a long-term economic function based on mixed-integer disciplined convex programming (MIDCP) which guarantees the sustainability of the building and its energy systems. The robust algorithm integrates risk management of intermittent sources, technical and economic parameters of selected technologies, and life cycle analysis (LCA) of different energy systems, including storage. Furthermore, the penetration of green hydrogen into the distributed generation mix is evaluated as an important contribution. Meteorological and energy demand variables of two antagonistic scenarios were also used as inputs to the algorithm. As a result, the optimal energy supply sizing for tertiary buildings in the two defined locations was obtained. The results of the simulations have achieved an optimal convergence of 100 % in the proposed scenarios, with a resolution time of 14 s and using a memory of about 183 MB. The simulations suggest a higher penetration of green hydrogen in scenarios where supply and investment costs decrease to gray hydrogen supply levels, reaching up to 81 % coverage of the thermal demand of the building. Hybrid energy systems under favorable conditions show a penetration of about 92 % within the distributed generation mix. The developed tool could enable decision-makers to optimally plan distributed generation projects in buildings based on economic, policy, and geographic conditions.

Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE

Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. However, conventional studies often focus solely on predicting single types of energy consumption and overlook the integration of physical laws and information, which are essential for a comprehensive understanding of energy dynamics. In this context, this paper introduces a multi-task physics-informed multi-gate mixture-of-experts (pi-MMoE) model that not only considers multiple forms of energy consumption but also incorporates physical principles through the integration of physical information and multi-task modeling. Specifically, a detailed analysis of manufacturing processes and energy patterns is first conducted to study various energy types and extract relevant physical laws. Next, using industry insights and thermodynamic principles, key equations for energy balance and conversion are derived to create a physics-based loss function for model training. Finally, the pi-MMoE model framework is constructed, featuring multi-expert networks and gating mechanisms to balance cross-task knowledge sharing and expert learning. In a case study of a textile factory, the pi-MMoE model reduced electricity and steam prediction errors by 14.28% and 27.27%, respectively, outperforming traditional deep learning methods. This demonstrates that the model can improve prediction performance, providing a novel approach to intelligent energy management and promoting sustainable development in manufacturing.

Techno-Economic Design and Optimization of Hybrid Energy Systems

Techno-Economic Design and Optimization of Hybrid Energy Systems

A Classical Approach for MPPT Extraction in Hybrid Energy Systems

A novel approach for Maximum Power Point Tracking (MPPT) extraction using the Hill Climbing method in hybrid solar and wind energy systems. MPPT is essential for optimizing the energy harvesting efficiency of sustainable energy sources, the integration of multiple sources poses unique challenges. The proposed Hill Climbing algorithm is applied to both solar photovoltaic (PV) panels and wind turbines, enabling efficient tracking of the Maximum Power Points (MPPs) under varying environmental circumstances. This article investigates the performance of the Hill Climbing MPPT method through simulation and experimental validation in a hybrid energy system. The algorithm's adaptability to the dynamic nature of solar irradiance and wind speed is analyzed, demonstrating its capability to rapidly converge to the MPPs for both solar and wind components. The integration of Hill Climbing MPPT for both sources enhances the overall energy harvesting efficiency of the hybrid system. The Hill Climbing MPPT method offers a robust and unified solution for hybrid solar and wind energy systems, providing improved performance and simplicity of implementation. The findings contribute to advancing the optimization of renewable energy systems by addressing the challenges associated with the simultaneous utilization of solar and wind resources.

High-Performance Optimised Porosity Iron Electrode for Hybrid Battery Electrolyzer

The growing demand for efficient, cost-effective and sustainable energy systems has prompted significant research into the development of advanced electrochemical devices. Hybrid battery electrolyzer, integrating the capabilities of both battery and electrolyzer, is a groundbreaking technology aiming to enhance both energy conversion efficiency and storage capacity. [1] Iron with large earth-abundance, low-cost, robustness, and high capacity is a promising electrode material for such hybrid energy systems. [2-4]This study focuses on the design, fabrication, and performance evaluation of a novel porous iron electrode tailored for use in a hybrid battery electrolyzer system. The proposed electrode exhibits superior electrocatalytic activity, enhanced mass transport properties, and increased surface area, contributing to improved overall performance. The electrode's porosity and pore structure are tuned to facilitate efficient mass transport and electrolyte penetration, addressing common challenges associated with conventional electrodes. The electrochemical performance of the optimised porosity iron electrode is investigated through comprehensive characterization techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and charge-discharge cycling tests. Results indicate superior electrochemical properties, showcasing enhanced specific capacity, faster charge/discharge kinetics, and prolonged cycle life compared to conventional electrodes. The incorporation of conducting nanocarbon and sintering at optimized temperature of 800 ℃ led to the achievement of a stable and elevated capacity exceeding 500 mAh/g for these advanced iron electrodes. The iron electrodes exhibit outstanding porosity (65 - 70%), along with a pore size of 5-10 µm. This configuration results in exceptional current rate performance, achieving a discharge capacity of 350 mAh/cm2 at a current density of 35 mA/cm2 and 245 mAh/cm2 at 50 mA/cm2. Furthermore, the iron electrodes exhibit superior performance in high-current electrolysis, showcasing an HER potential of -1.25, -1.34, and -1.50 V (vs Hg/HgO) at a current density of 200, 500 and 1000 mA/cm2 respectively.These findings highlight the potential of optimised porosity iron electrodes as a key component in the development of high-performance and cost-effective hybrid battery electrolyzer systems. The integration of such advanced materials holds promise for unlocking new frontiers in energy storage and green hydrogen technology, contributing to the realization of efficient and sustainable energy systems.

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value.In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M ± 1.09M USD and 104.99M ± 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

Hierarchical optimization for the energy management of a greenhouse integrated with grid-tied photovoltaic–battery systems

This study addresses the challenges of high energy consumption and environmental concerns in traditional greenhouse operations by exploring an integrated greenhouse with grid-tied photovoltaic (PV)-battery systems. A two-layer hierarchical optimization framework is proposed for effective energy management. In the upper layer, greenhouse operations are optimized with the Energy Consumption Minimization (ECM) strategy and the Energy Cost Minimization (ECoM) strategy, ensuring suitable climate conditions while reducing energy use and costs. In the lower layer, the scheduling of the hybrid energy system is optimized with the Self-consumption Maximization (SCM) strategy and the Total Cost Minimization (TCM) strategy, aiming to maximize the self-consumption of PV power and minimize total costs, including energy costs, battery aging costs, and carbon emission costs, while meeting the greenhouse’s electricity demand. A sensitivity analysis is conducted to investigate the impact of electricity prices, feed-in tariffs, battery capacity, and PV array area on hybrid energy system scheduling. Results reveal that the ECoM strategy reduces costs by 6.98% compared to the ECM strategy, and the TCM strategy reduces total costs by 43.50% compared to the SCM strategy. Battery capacity and PV array area are found to have a more significant influence on total costs than electricity prices and feed-in tariffs. This study can offer crucial insights for realizing cost-effective and environmentally friendly greenhouse operations, contributing to the advancement of sustainable agriculture.

Improving the performance of PV/diesel microgrids via integration of a battery energy storage system: the case of Bilgo village in Burkina Faso

BackgroundPV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems.MethodsThis study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software.ResultsIt was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy.ConclusionsIt can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage.

Optimization and design to catalyze sustainable energy in Morocco’s Eastern Sahara: A hybrid energy system of PV/Wind/PHS for rural electrification

This paper conducts a comprehensive assessment of the potential of water, solar, and wind resources for sustainable energy generation. The study is situated in a Moroccan region within eastern Saharan Africa. It presents a detailed comparative analysis between a photovoltaic system (PV) integrated with a pumped hydro storage (PHS), a wind turbine, and a conventional grid, considering both energy production and economic analysis using HOMER software. Moreover, the paper provides an initial social impact assessment of hybrid energy systems integrating locally available water resources, especially during the winter season, alongside photovoltaic and wind technologies. This evaluation delves into aspects of rural electrification and community development. The findings underscore the potential of sustainable energy solutions to drive economic and social progress in the studied area by harnessing the region’s water resources. We proposed this technology because the owners of the area do not greatly benefit from the seasonal groundwater that passes through the valley, despite the presence of a dam. Accordingly, we will exploit this water to generate energy and achieve energy self-sufficiency. By harnessing this underutilized resource, we aim to provide sustainable energy solutions and drive economic and social progress in the region. The results given by HOMER identify the most cost-effective system capable of serving the load at the lowest cost of energy (COE) of about $0.03831 and net present cost (NPC) of about $262,596 under the modeled conditions, and the most satisfactory system chosen by the HOMER optimizer is a PV/Wind/PHS-based hybrid energy system.

A mathematical model of an off-grid hybrid energy system utilizing the reversible solid oxide fuel cell

Abstract The current energy transition focuses on decentralizing energy systems by implementing alternative energy sources. Integrating distributed energy sources into functional hybrid energy systems has proven to be a difficult task. To solve this problem, it is important to conduct an in-depth analysis of the energy system through the preparation of a mathematical model. The study aims to model and simulate the operation of an off-grid self-sustainable energy storage system for single-family household use by utilizing solid oxide technology. The proposed system consists of photovoltaic modules and an energy storage system including batteries, reversible solid oxide fuel cells, and hydrogen storage tanks. To integrate these energy resources into one system, an energy management system, in which the solid oxide stack is used as a backup generator or a hydrogen production device, is implemented. Finally, a numerical simulation of the system’s operation is run to estimate the system’s performance over a one-year time period. The prepared model can find many practical applications, such as optimizing system costs, environmental impact, or the energy management system.

Analyzing energy transition for industry 4.0-driven hybrid energy system selection with advanced neural network-used multi-criteria decision-making technique

This study aims to select the appropriate renewable energy alternatives for the efficiency of hybrid energy systems to increase energy transition performance. For this purpose, a novel neural network (NN)-based fuzzy decision-making model is constructed that has three different stages. In the first stage, NN-based fuzzy decision matrix is created. Secondly, 6 different variables based on industry 4.0 are weighted with the sine trigonometric Pythagorean fuzzy entropy technique. Additionally, another calculation has been implemented with criteria importance through intercriteria correlation (CRITIC) to identify the consistency of the results. Furthermore, in the third stage, considering 5 different renewable energy alternatives, 10 different combinations are identified for hybrid energy systems. The most effective alternatives are defined by the sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) method. Moreover, to test the validity of these results, another analysis is conducted using the additive ratio assessment (ARAS) technique. The main contribution of the study is that the optimal renewable energy combination required for an efficient hybrid energy system is determined by performing a priority analysis between the variables. This situation has a significant guiding feature for investors. Similarly, the development of the RATGOS technique both increases the methodological originality of the study and enables more accurate alternative ranking. It is identified that the results of all methods are similar. Therefore, this situation gives information about the coherency and validity of the findings. It is concluded that the most important criterion is real-time capability. It is also denoted that the best combination for hybrid energy systems is Solar-Wind.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Optimizing Hybrid Energy Systems for Sustainable Development in the Canadian Arctic: A Case Study of Arviat, Nunavut

Optimizing Hybrid Energy Systems for Sustainable Development in the Canadian Arctic: A Case Study of Arviat, Nunavut

Strategy of geothermal energy development as a renewable energy source in West Java Indonesia

Background: Indonesia has vast renewable energy potential, including biofuels, biomass, and bioenergy from tropical biodiversity spread across the country. Hydropower and geothermal energy are the only forms of renewable energy used to generate electricity and are connected to the grid. Geothermal energy may be incorporated into the grid to create hybrid energy systems that will help to reduce the high energy demand while maintaining low energy costs and net present costs. There are now 14 biosphere reserves in Indonesia, divided into 24 units of core zones, six units of buffer zones, and 13 units of transition zones. The West Java Province has a geothermal potential of 6,101 MWe or 21% of Indonesia's total geothermal resources. Currently, the installed capacity of electricity from geothermal energy in West Java is 1075 MWe or 89% of the total national installed capacity of 1196 MWe. Method: This paper reviews West Java Province data collection from official government bureaus, state-owned businesses, and non-governmental organizations (NGO) reports on geothermal energy capacity, electricity installation, and used area of ​​geothermal power plant and will be limited to the years 2010 through 2020. The collected data will then be refined, extrapolated, and analyzed by the connected trend to aid in studying West Java geothermal growth and use SWOT method analysis. Finding: The result of this research is a strategy for optimizing the development of geothermal energy utilization. The strategies that can be developed are infrastructure improvement as an investment facilitation strategy, a strategy to leverage a costly investment and capital investment, underground mining in conservation forest areas to avoid degradation improvement and supervision of related institutions and stakeholders, implementation of environmentally sustainable development, and socializing programs and providing job opportunities for the local community. Conclusion: Indonesia has an enormous potential for renewable energy, especially geothermal, with West Java Province having the largest installed capacity. This study suggests strategies to optimize geothermal energy development, including improving infrastructure, investment facilities, underground mining, improving supervision, and sustainable development. Novelty/Originality of this study: Research on geothermal energy in West Java provides a comprehensive analysis and specific strategies to optimize the development of this energy in the area, including innovative underground mining approaches.