Availability Of Renewable Resources Research Articles

Role of Nanomaterials in Degradation of Cosmetics and Pharmaceutical Wastewater: A Review

Biodiversity progress impacts the quality and availability of renewable natural resources. Ecological concerns about air and water pollution have been raised on communal and commercial levels. The continuous release and accumulation of cosmetics and pharmaceutical items in the environment pose significant threats to both ecosystems and human health. The presence of these emerging pollutants from pharmaceutical and cosmetic products has been reported in wastewater streams coming from industrial as well as wastewater treatment plants. This need has driven the development of technologies like advanced oxidation processes such as ozonation, Fenton’s oxidation, photocatalysis, etc. for conventional wastewater treatments. Among these, the photocatalytic processes are presently being efficient since they not only utilize UV light but also visible light. This article mainly focuses on the review of preparation and comparative activity of photocatalysts on cosmetic as well as pharmaceutical wastes degradation. Photocatalysts, in their different forms like nanoparticles, nanorods and nanocomposites, hold great promise due to their stability, non-toxicity and reusability.

A non-isolated converter of switching current stress in DC-DC converter for integrating PV and battery storage system

ABSTRACT High-gain DC-DC converters have seen significant adoption in renewable energy systems like Photovoltaic (PV), Fuel Cells (FC), and other systems. However, their effectiveness is increasingly tied to the availability of renewable resources. As Renewable Energy Sources (RES) typically generate electricity at low voltage levels, integrating high-gain DC-DC converters with RES is crucial for optimal performance. This paper introduces an innovative DC-DC converter design that achieves high gain and is specifically able to integrate smoothly with RES like PV systems and battery storage. The innovation of the proposed converter lies in its integration of conventional boost and buck-boost converters, enabling a high voltage gain while minimizing output voltage ripple and reducing switch current stress. Additionally, its unidirectional power transfer port allows efficient energy flow from solar PV or battery sources to the load, making it highly adaptable for managing fluctuating energy sources, a feature that distinguishes it from current modern converters. Its design simplifies architecture by combining traditional converter types, reducing the number of components, which leads to cost savings, lower weight, and improved reliability. Additionally, the design minimizes output voltage ripple and reduces switching current stress, enhancing the converter’s durability under variable operating conditions. The converter’s performance is evaluated through simulation and experimental studies, demonstrating a 96.3% efficiency and 5.5 times of voltage gain. The converter’s feasibility and effectiveness are validated under dynamic scenarios with varying solar irradiation, making it an ideal solution for clean energy generation systems, including hybrid energy generation setups. The proposed converter topology offers an auspicious solution for high-gain DC-DC conversion in renewable energy systems.

Framework for strategic deployment of hybrid offshore solar and wind power plants: A case study of India

Renewable energy sources are gaining prominence as eco-friendly and sustainable alternatives to fossil fuels due to their availability and minimal greenhouse gas emissions. Nonetheless, the critical challenge is the availability of renewable resources, which fluctuates with changes in climatic conditions. This limitation poses a consistent challenge to generating base load power if it relies solely on a single type of renewable resource. Addressing this, integrating multiple renewable sources into hybrid systems has emerged as a viable solution. This study presents a framework, integrating Geographic Information Systems (GIS) and Hybrid Multi-Criteria Decision Making (MCDM) techniques to identify plausible locations for the deployment of Hybrid Offshore Solar and Wind Power Plants (HOSWPP) and the developed framework is demonstrated considering Indian Exclusive Economic Zone (EEZ) as a study area. Using the proposed approach, Indian EEZ region is classified into five suitability classes. The effectiveness of regions within each class is further assessed in terms of complementarity measured using Kendall's coefficient. Findings suggested that Kendall's coefficient for highly suitable class is −0.41 indicating the regions identified in this study are the prime locations for installing HOSWPP. A total of twenty optimal sites for HOSWPP deployment, predominantly in the offshore regions of Tamil Nadu and Gujarat. Eighteen sites are located along Kanyakumari to Thisayanvilai in Tamil Nadu, including areas in the Gulf of Mannar and near Valinokkam are found plausible. The rest of the two sites are in the offshore regions of Gujarat. This study provides a strategic roadmap to increase the renewable footprint, contributing to the global transition towards cleaner energy sources.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Integrating liquid hydrogen infrastructure at airports: Conclusions from an ecosystem approach at Rotterdam The Hague Airport

Aviation is a contributor to global warming. Hydrogen-powered aircraft are seen as an important option to decarbonise parts of commercial aviation. Airports have a pivotal role in facilitating the development of ground infrastructure. This paper provides a broader perspective on the supply and handling of liquid hydrogen, and necessary airport developments, to enable hydrogen-powered aviation. Hydrogen-related airport development projects at Rotterdam The Hague Airport (RTHA) are presented and discussed, and a detailed overview of the airport’s liquid hydrogen (LH2) storage facility is given. To link the ongoing developments to future needs, a LH2 demand scenario for RTHA is determined for the years 2040 and 2050. Based on this demand, analysis of levelised cost of hydrogen for relevant value chains were conducted. This study exemplifies that the LH2 value chain for an airport depends on individual characteristics of the airport and its surroundings. The hydrogen demand, the airport’s proximity to larger hydrogen hubs (import and/or production hubs) and the availability of local renewable resources, which influence electricity price and hydrogen production and liquefaction costs, are key parameters and heavily influence the airport LH2 value chain. Conceptualisation and future development of hydrogen infrastructure for airport supply should take into account the above factors. LH2 demand at RTHA in the year 2050 is predicted to range between 8–14kt. Under the given electricity price assumptions, local production and liquefaction of hydrogen at the airport is not seen as a viable option, as cost savings can be achieved by making use of the Port of Rotterdam’s large hydrogen production and import cluster nearby. The work shows that trailer-based logistics for both the delivery of LH2 to the airport and subsequent usage of these trailers in the storage and dispensing process at the airport seems the most viable for RTHA (and airports that show similarities). This further indicates that current small-scale LH2 demonstration at airports provides important lessons for scaling up.

Harnessing artificial intelligence for enhanced bioethanol productions: a cutting-edge approach towards sustainable energy solution

Abstract The adoption of biofuels as an energy source has experienced a substantial increase, exceeding the consumption of fossil fuels. The shift can be ascribed to the availability of renewable resources for energy production and the ecological advantages linked to their utilisation. Nevertheless, due to its intricate characteristics, the process of producing ethanol fuel from biomass poses difficulties in terms of administration, enhancement, and forecasting future results. To tackle these difficulties, it is crucial to utilise modelling techniques like artificial intelligence (AI) to create, oversee, and improve bioethanol production procedures. Artificial Neural Networks (ANN) is a prominent AI technique that offers significant advantages for modelling bioethanol production systems’ pretreatment, fermentation, and conversion stages. They are highly flexible and accurate, making them particularly well-suited. This study thoroughly examines several artificial intelligence techniques used in bioethanol production, specifically focusing on research published in the past ten years. The analysis emphasises the importance of using AI methods to address the complexities of bioethanol production and shows their role in enhancing efficiency and sustainability in the biofuel industry.

Forecasting the ecological footprint of G20 countries in the next 30 years

The Ecological Footprint evaluates the difference between the availability of renewable resources and the extent of human consumption of these resources. Over the past few decades, historical records have shown an accelerated decline in the availability of resources. Based on national footprint and biocapacity accounts, this analysis aims to advance the forecasting of the G20 countries' ecological footprints over a 30-year time frame. We employed a time series forecasting approach implemented in Python, which included-modular regression (Prophet) and Autoregressive Integrated Moving Average (ARIMA & Auto-ARIMA) methods. We evaluated and combined the performance of these three methods. The results indicated that among the largest economies of the G20, only four countries are projected to have a positive ecological footprint balance by 2050. These countries share the common denominator of large land areas and a moderate population growth projection. However, the overall trend of the indicator suggests that it will continue to decline.

Spatio-temporal distribution of renewable freshwater resources and their availability in Kosovo—an analysis from the Eastern Region

The study aims to analyze the availability of renewable freshwater resources and their spatiotemporal distribution in the Eastern Region of Kosovo (ERK). As a crucible resource for agricultural, industrial, and commercial activities, water scarcity will lead to water shortages. Kosovo is a landlocked country, and ERK is the most important region for industrial activities (coal, mines) and agricultural use (plains). Located in continental climate conditions, surrounded by middle to high-altitude mountains, the region does not have a favorable spatio-temporal distribution of freshwaters. The determination of the water resources is based on the amount of surface runoff, which being divided per capita, gives indications of the water resources of a region (catchment). For our aims, main river discharge and population statistics are used to analyze the spatiotemporal distribution and availability of renewable freshwater in ERK. Falkenmark Freshwater Indicator with 1.483 m³/capita/year shows that ERK lies under water scarcity, and existing water reservoirs offer low security for water withdrawals, and further reservoirs would prevent water scarcity in the region.

Optimizing energy solutions: A techno-economic analysis of solar-wind hybrid power generation in the coastal regions of Bangladesh

Hybrid renewable energy systems have acquired attention worldwide for their ability to harness multiple renewable sources parallelly like solar, wind, and hydropower, presenting numerous advantages. Bangladesh is forced to rely on the traditional fossil fuel-powered power generation infrastructure in order to meet the nation’s increasing need for electricity. The goal of this paper is to improve the percentage of renewable energy in Bangladesh’s energy landscape by addressing the technical, economic, and environmental elements of building a specialized hybrid system at Patenga Sea Beach through a methodical approach. This study is the first to pinpoint and address Patenga Sea Beach’s limits in light of Shah Amanat International Airport’s nearby location in Chittagong. In order to build the suggested system, a daily load demand of 1000 kWh and an annual peak demand of 53.57 kW were taken into account. The analysis shows that this system has a levelized cost of energy (LCOE) of 0.03$, generating 4,604.591 MWh of power annually. Findings from HOMER reveal an initial capital outlay of $350,688 for initial capital investment, with an annual O&M cost of $3,821, contributing to its cost-effectiveness. During the span of its lifetime, the hybrid system can also avoid 5,767 tons of CO2. The simulation program PVsyst has been utilized to assess the PV system-specific performance, viability, and outcomes. According to the PVsyst evaluation, the PV system has an 89 % system performance ratio, a 0.017$ levelized cost of energy, and a break-even period of only 6.6 years. The sensitivity analysis examines renewable resources, factoring in climate change’s effects on solar irradiation, wind speed, replacement cost, and operational expenses. It validates the hybrid system’s viability across various environmental scenarios, demonstrating resilience to changes in renewable resource costs and availability. The suggested hybrid system can be placedto minimize costs associated with energy generation, circumvent structural and geographic limitations, improve the Patenga Sea beach’s visual appeal, and lower greenhouse gas emissions. In addition to supporting future decisions about the deployment of hybrid systems, this research could assist in the integration of sustainable and renewable energy sources into the national grid.

How demand-side management can shape electricity generation capacity planning

Forward-looking strategies for Demand Side Management (DSM) can accelerate the clean energy transition and reduce carbon emissions if electricity load can be shaped to be better matched to the hourly availability of renewable resources. Time-varying rates are increasingly used to motivate load shifting, capitalizing on the increasing size of loads that can be easily shifted, such as electric vehicle charging and electric heat pumps. This study analyzes the hourly availability of current and renewable resources for two representative electricity grids in the USA. While many demand-management programs are currently designed to shift load to nighttime when demand is low, to use low-cost PV the opposite shift into daytime will be preferred. Given that investments in electricity generation capacity shape the grid for decades, generation capacity planning should proactively anticipate how DSM programs (and associated load shapes) will change as the generation mix changes. These future load shapes determine the optimal resources to be acquired and can be modified by DSM to facilitate variable renewable integration. Ongoing regulatory proceedings in Colorado are described as a case study for an existing decision-making process.

Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

In recent years, growing interest has emerged in investigating the integration of energy storage and green hydrogen production systems with renewable energy generators. These integrated systems address uncertainties related to renewable resource availability and electricity prices, mitigating profit loss caused by forecasting errors. This paper focuses on the operation of a hybrid farm (HF), combining an alkaline electrolyzer (AEL) and a battery energy storage system (BESS) with a wind turbine to form a comprehensive HF. The HF operates in both hydrogen and day-ahead electricity markets. A linear mathematical model is proposed to optimize energy management, considering electrolyzer operation at partial loads and accounting for degradation costs while maintaining a straightforward formulation for power system optimization. Day-ahead market scheduling and real-time operation are formulated as a progressive mixed-integer linear program (MILP), extended to address uncertainties in wind speed and electricity prices through a two-stage stochastic optimization model. A bootstrap sampling strategy is introduced to enhance the stochastic model’s performance using the same sampled data. Results demonstrate how the strategies outperform traditional Monte Carlo and deterministic approaches in handling uncertainties, increasing profits up to 4% per year. Additionally, a simulation framework has been developed for validating this approach and conducting different case studies.

Assessing the impact of renewable energy penetration and geographical allocation on transmission expansion cost: A comparative analysis of two large-scale systems

Ambitious renewable energy development plans require an efficient electricity grid connection of massive generation capacity. Significant transmission network investments are considered necessary to support the energy transition. This paper analyses the transmission grid reinforcement requirements of two large-scale electricity systems with notably different geographical distributions of renewable resource availability and load density. The results indicate that optimal transmission expansion accounts for less than 10% of total annualised system investment (generation capacity + transmission grid). This is true even in transmission systems with significant existing bottlenecks. The study includes a sensitivity analysis of the location of renewable energy source (RES) capacity. The sensitivities examine the impact of varying the concentration of RES in resource-rich areas, as well as the proportion of RES capacity connected at the distribution level. Both sensitivities are evaluated over a common base case scenario. The results show that a higher concentration of RES capacity in resource-rich network zones can more than double the optimal transmission expansion requirements. In contrast, the implementation of distributed generation (DG) leads to the allocation of generation closer to demand centres, resulting in transmission grid savings of up to 30%. These effects are more pronounced in networks where RES capacity is located further away from major demand centres. This is because existing bottlenecks are exacerbated by higher RES concentration and relieved as the share of DG increases.

Renewable green oil as a low-cost option for large-scale, long duration energy storage to produce on-demand electricity

<p class="MsoNormal" style="text-align: justify;"><span style="font-size: 14pt; font-family: 'times new roman', times, serif;">The rapid growth of renewable solar and wind energy necessitates extensive, long-term energy storage solutions to stabilize the electric grid and address the intermittent nature of these sources. In the absence of cost-effective long-duration, large-scale (LDLS) energy storage, there is a risk of increased disruptive blackouts and brownouts as intermittent energy sources approach 50% of electricity generation. Our analysis underscores volumetric energy density as a pivotal determinant in LDLS energy storage solutions, which is particularly crucial during periods of diminished renewable resource availability, such as the rainy season. Despite emphasis on conventional electric batteries and mechanical storage technologies such as compressed air and pumped hydro for LDLS, their costs pose a significant obstacle to widespread adoption, especially in emerging economies. In this study, we present a groundbreaking approach to LDLS: on-demand electricity generation from carbon-neutral renewable oil, known as "green oil," leveraging Reliance’s innovative catalytic hydrothermal liquefaction (R-CAT-HTL) technology. Our investigation evaluated two distinct sources of green oil production: organic waste streams (e.g., municipal solid waste, agricultural-waste, etc.) and on purpose production of algal biomass. We demonstrate that burning renewable green oil to produce electricity offers a cost-effective solution for large-scale (GWh) long-duration (many days) energy storage, ensuring the stability, reliability, and resiliency of the electric grid. We also demonstrate that minimum 14 days, ideally 30 days energy storage is needed for continuous uninterrupted service, especially critical for manufacturing facilities. Through a meticulous levelized cost of storage (LCOS) analysis comparing green oil with other LDLS technologies reported in literature, we demonstrate the cost effectiveness of green oil. In this model, green oil serves as stored energy, functions akin to a battery and is efficiently converted into electricity through conventional reciprocating engines or next-generation more efficient turbines. Our findings suggest that green oil derived from diverse organic sources is the most economically viable option for GWh-scale storage, particularly when storage requirements extend beyond a single day. This research thus presents a transformative cost-effective solution to the pressing challenge of LDLS energy storage, accelerating a sustainable and resilient energy future.</span></p>

Green retailer: A stochastic bi-level approach to support investment decisions in sustainable energy systems

This paper presents a bi-level approach to support retailers in making investment decisions in renewable-based systems to provide clean electricity. The proposed model captures the strategic nature of the problem and combines capacity sizing decisions for installed technologies with pricing decisions regarding the electricity tariffs to offer to a reference end-user, representative of a class of residential prosumers. The interaction between retailer and end-user is modeled using the Stackelberg game framework, with the former acting as a leader and the latter as follower. The reaction of the follower to the electricity tariff affects the retailer’s profit, which is calculated as the difference between the revenue generated from selling electricity and the total investment, operation and management costs. To account for uncertainty in wholesale electricity prices, renewable resource availability and electricity request, the upper-level problem is formulated as a two-stage stochastic programming model. First-stage decisions refer to the sizing of installed technologies and electricity tariffs, whereas second-stage decisions refer to the operation and management of the designed system. The model also incorporates a safety measure to control the average profit that can be achieved in a given percentage of worst-case situations, thus providing a contingency against unforeseen changes. At the lower level, the follower reacts to the offered tariffs by defining the procurement plan in terms of energy to purchase from the retailer or potential competitors, with the final aim of minimizing the expected value of the electricity bill. A tailored approach that exploits the specific problem structure is designed to solve the proposed formulation and extensively tested on a realistic case study. The numerical results demonstrate the efficiency of the proposed approach and validate the significance of explicitly dealing with the uncertainty and the importance of incorporating a safety measure.

Evaluation and deployment of a unified MPPT controller for hybrid Luo converter in combined PV and wind energy systems

This work emphasizes the development and examination of a Hybrid Luo Converter integrated with a unified Maximum Power Point Tracking (MPPT) for both grid and independent hybrid systems. The primary objectives of this hybrid system are to efficiently harness power from intermittent and variable renewable sources while elevating low-voltage energy inputs to utility-grade levels. Unlike previous studies employing specific MPPT algorithms for solar and wind sources, this work aims to simplify the control system by utilizing a unified MPPT controller. This research also introduces a novel approach involving dual-lift hybrid Luo converters to create hybrid systems, operating exclusively or concurrently based on the availability of renewable resources. To maximize power generation from all renewable sources, a unified MPPT algorithm is developed. The hybrid system, incorporates 500 W wind and 560 W PV systems, the innovative Luo converter, and the unified MPPT controller. A comprehensive comparative analysis is presented, comparing the hybrid system's performance with that of traditional control algorithms, such as the Perturb & Observe, and Radial Basis Function Network controllers. The successful prototype of the converter validates the practicality of the proposed approach.

Selection of a sustainable location for waste electrical and electronic equipment recycling plant using Entropy and Evaluation based on Distance from Average Solution.

The production and consumption of electronic goods have experienced a significant increase over the years, leading to a substantial surge in the global volume of electronic waste, commonly referred to as Waste Electrical and Electronic Equipment (WEEE). The selection of a sustainable location for WEEE recycling plants plays a crucial role in mitigating environmental concerns, preserving resources and promoting economic development. It signifies a proactive and responsible approach to electronic waste management in the contemporary world. To tackle the challenge of selecting sustainable locations for WEEE recycling plants, this study employed the Entropy and Entropy and Evaluation based on Distance from Average Solution (EDAS) methodologies, evaluating 10 alternative cities in Turkey based on 13 criteria. The selected criteria include land cost, personnel cost, energy cost, availability of labour, government support degree, tax preferences, road network accessibility, number of electronic equipment producers, existence of recycling plants, suitability of land use, population and availability of renewable resources (wind power and solar energy). The Entropy method was employed to calculate the weights assigned to each criterion, whereas the EDAS method was utilized to evaluate the decision alternatives. The results provide region-specific recommendations, such as Antalya for the Mediterranean region and Samsun for the Black Sea region. The literature lacks sufficient research on the selection of sustainable locations for WEEE recycling plants. Furthermore, the utilization of real data enhances the study's credibility and provides practical insights for decision-making. The selection of a sustainable location for a WEEE recycling plant in Turkey not only demonstrates the country's environmental commitment but also sets a global example for responsible waste management.

Renewable Energy Integration in Buildings A Case Study in Portugal

This paper aims at the presentation of a proposal for the integration of solar thermal technology in buildings, particularised for the Portuguese context through a case study, a new sport public building in Coimbra, which consists of two swimming pools, gym, jacuzzi and sanitary facilities. This proposal has been developed in the framework of a Master dissertation currently under development, which the main objective is the selection of the most appropriate technological solution for the integration of renewable energy in buildings. In this paper we propose the selection of the most appropriate technological solution for the integration of three thermal solar systems in the new building. Two systems are used for heating the swimming pools and the third one is dedicated to domestic hot water. The selection of the best solution will take into account the constructive aspects of the building, the building's energy needs and the local availability of renewable resources and market technologies. For each solar thermal system, the general scheme of the proposed technical solution is presented. Investment costs and economic viability of the selected solutions are also presented.

A stochastic two-stage microgrid formation strategy for enhancing distribution network resilience against earthquake event incorporating distributed energy resources, parking lots and responsive loads

The sustainability of energy systems has been severely affected in recent years by the quantity and intensity of high-impact, low-probability events. As a result, the resilience concept has been introduced to analyze the operation of networks against such events. This paper proposes a stochastic two-stage microgrid formation formulation to maximize the restored loads and enhance the resilience index of electrical distribution systems in the face of earthquake events. In this regard, an earthquake incident is modeled in the first step utilizing geographical data, peak ground acceleration and fragility curves. Then, a two-stage linear programming and graph theory is applied to form microgrids in response to the mentioned disturbances, where the variables in the planning and operating states are optimized in the first and second stages, respectively. The influence of different components including diesel generators, renewable energies, storage units and electric vehicles is investigated. In addition, the role of responsive loads and uncertainties related to line failure, load variation, renewable resources availability and electric vehicles are considered during the analysis. The results for various scenarios validate that the presented approach increases load supply capability and resiliency index by 38.7% and 38.8%, respectively.

Neutrosophic Multi-Criteria Decision-Making Framework for Sustainable Evaluation of Power Production Systems in Renewable Energy Sources

To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these factors. So, we used the multi-criteria decision-making (MCDM) approach VIKOR to assess the power production systems in the sustainability criteria. The VIKOR method is used to rank the several alternatives. We integrated the neutrosophic set with the VIKOR method to deal with inconsistent information. We used seven criteria and ten alternatives in this study. The development of a sustainable and resilient energy sector is aided by the sustainable evaluation of power production systems, which in turn helps to reduce the effects of climate change and safeguard the environment.

Offshore Wind Power Potential in Brazil: Complementarity and Synergies

Renewable sources stand out in energy planning due to their contribution to greenhouse gas emission reduction when displacing fossil fuels and the enhancement of energy security through the diversification of the energy matrix. Understanding and optimizing the complementary operative synergy between different energy sources over time and space leads to efficient policies. This article uses an hourly Pearson’s correlation coefficient to explore the complementarity between offshore wind and other power generation sources in the Brazilian matrix. An analysis of offshore wind power feasibility in the Brazilian power system will be conducted, considering environmental implications, synergies with the oil industry, costs, and complementarities with other energy sources. The methodology uses an optimization model to minimize costs and optimize the production mix while considering the time series of renewable energy, subject to demand constraints, renewable resource availability, reservoir storage, capacity limitations, and thermal generation. The study concludes that the northeast and southeast electrical subsystems must start offshore wind installation in Brazil due to their complementarity with hydropower production, synergy with the oil and gas industry, and proximity to the largest consumption spots.