Renewable Energy Engineering Solutions

Scientists developed a technology that turns transparent surfaces into solar panels. Soon, your windows could be generating power for your home. Researchers at Michigan State University have developed a transparent luminescent solar concentrator—a thin, plastic-like material that can be applied to windows, car windshields, and even mobile devices to generate electricity from invisible wavelengths of sunlight. This innovation, reported in Nature Energy, could complement traditional rooftop solar panels, significantly increasing the total energy captured from the sun. Scientists estimate that widespread use of transparent solar materials could meet nearly 40% of U.S. electricity demand, helping reduce reliance on fossil fuels. Although transparent solar cells currently operate at lower efficiencies (above 5%) compared to traditional panels (15-18%), they offer a vast amount of untapped surface area for energy harvesting. With around 5 to 7 billion square meters of glass available in the U.S., this technology has the potential to transform the way we generate clean energy. Researchers believe that combining transparent solar applications with rooftop solar and improved energy storage solutions could push the nation closer to 100% renewable energy. With ongoing advancements, this breakthrough represents a promising step toward a more sustainable future.

Researchers at the University of California, Berkeley , have developed a material named COF-999, a fluffy yellow powder capable of capturing carbon dioxide (CO₂) from the atmosphere with remarkable efficiency. Just under half a pound (approximately 200 grams) of COF-999 can absorb about 44 pounds (20 kilograms) of CO₂ annually, matching the carbon sequestration capacity of a mature tree over the same period. COF-999 is a covalent organic framework (COF) characterized by its porous structure, which provides a large surface area for gas adsorption. The internal surfaces of this material are lined with amines—compounds that effectively bind to CO₂ molecules. When air passes through COF-999, the amines capture CO₂, and the gas can later be released by heating the material to about 140°F (60°C), allowing for repeated use. Notably, COF-999 has demonstrated stability over at least 100 adsorption-desorption cycles without degradation. This innovation holds significant promise for direct air capture (DAC) technologies, which aim to reduce atmospheric CO₂ levels to mitigate climate change. The efficiency and durability of COF-999 could enhance the viability of DAC systems, potentially accelerating efforts to lower greenhouse gas concentrations in the atmosphere. Source: https://lnkd.in/dgwRRzhe

NEW RESEARCH - WHY THE ENERGY TRANSITION IS DISRUPTIVE & COULD BE MUCH FASTER THAN WE THINK: The clean energy transition isn’t just about swapping out old tech for new—it’s a complex, non-linear process full of feedback loops, tipping points, and unexpected consequences. Our new “Systems Archetypes of the Energy Transition” brief is a must-read for anyone shaping policy, investing, or innovating in this space. Key takeaways: 1) Feedback loops drive change: Reinforcing loops (like learning-by-doing and economies of scale) have made solar, wind, and batteries cheaper and more widespread, often outpacing even the boldest forecasts. 2) Path dependence is real: Early advantages for a technology (think BEVs vs. hydrogen cars) can snowball into market dominance, making policy choices and timing critical. 3) Limits and synergies: As renewables grow, market dynamics like “cannibalisation” can dampen investment—unless we design markets and storage solutions to keep the momentum going. 4) Policy design is everything: Well-intentioned fixes (like price caps or broad subsidies) can backfire, while smart, targeted interventions can unlock positive feedbacks across sectors. 5) Tipping points and decline: The decline of fossil fuels isn’t just a mirror image of clean tech growth—it comes with its own feedbacks, risks, and opportunities for a just transition. The brief also offers practical guidance on using causal loop diagrams and participatory systems mapping—powerful tools for understanding and managing the complexity of the transition. If you’re working on energy, climate, or innovation policy, I highly recommend giving this a read. Let’s move beyond linear thinking and embrace the systems view—because the future will be shaped by those who understand the dynamics beneath the surface. This briefing was led by Simon Sharpe at S-Curve Economics CIC, Max Collett 柯墨, Pete Barbrook-Johnson, me at Environmental Change Institute (ECI), University of Oxford & Oriel College, Oxford & the Regulatory Assistance Project (RAP) and Michael Grubb at UCL Institute for Sustainable Resources.

The energy transition is more than just a shift to renewables; it’s a total reinvention of our infrastructure, with electricity distribution networks acting as vital enablers of this change. Electricity is the best vector for decarbonization, and the world increasingly relies on it. Effectively these networks expand, must be capable of supporting renewable integration, but they must also be optimized for digital innovation, efficiency, and sustainability. This is where Electricity 4.0 plays a transformational role. The concept of Electricity 4.0 assumes massive electrification in tandem with deployment of digital intelligence within electric systems, turning traditional distribution networks into smart, responsive systems. These networks don’t just distribute power—they actively manage, monitor, and adapt in real-time, creating an energy ecosystem that is reliable, efficient, and more sustainable. One compelling example of making progress is the adoption of SF6-free medium-voltage (MV) switchgear. In our case it’s AirSeT. Let me recap how it fits into the bigger picture: 1. Integrating renewables at scale: Distributed renewables need robust networks to balance power flows dynamically and manage fluctuating demands. AirSeT is equipped with CompoDrive, 10x stronger than its predecessor to accommodate massively increasing switching requirements. 2. Optimizing energy management through digitalization: By embedding IoT and AI, we can achieve real-time monitoring and predictive maintenance, minimizing losses and boosting efficiency. Switchgear needs powerful digital capabilities to gather intelligence from the field. 3. Sustainable infrastructure with sustainable MV solutions: SF6-free minimizes CO2e footprints while ensuring network reliability. Each kilogram avoided means 24,300 kg of CO2e less in the networks. Operational life extended by up to 30% and no toxic byproducts of breaking support circularity. The journey toward a low-carbon economy demands more than just clean power generation; it requires revolutionary approaches to how energy is managed, distributed, and optimized. Electric distribution networks aren’t just supporting the transition—they’re driving it, like Drakenstein Municipality in South Africa. Let’s continue to lead this transformation, ensuring every step forward brings us closer to a resilient, sustainable energy future. Read this eBook to discover how SF6-free and digital solutions enable decarbonization and efficiency: https://lnkd.in/dGThND2Q #SF6Free #LifeIsOn #AirSeT

An energy boost for the European energy transition: the reform of the EU Electricity Market Design has now been adopted – and it is positive news!    Our single European electricity market is an immense feat and key to a successful energy transition throughout Europe. This reform reinforces this with many beneficial components. Progress and simplifications have been made where needed, whilst potentially damaging aspects have been placed under review. Some question marks remain, but now, we are getting somewhere.    So, what exactly has changed? As you can imagine, many technical terms, whose acronyms comprise useful amends and mechanisms.   ▪The option to apply inframarginal revenue caps will not be extended – a policy that harmed not aided the energy transition through creating investment uncertainty for renewables.    ▪Two-sided Contracts for Difference (CfDs) will now be the standard for direct state support and Power Purchase Agreements (PPAs) will be strengthened. Both are important long-term instruments for investments in renewables.     ▪ Capacity Remuneration Mechanisms (CRM) will no longer just be considered a last resort, with the approval process also to be simplified. These schemes ensure capacities are available and ready to generate electricity for the grid, making sure customer demand for electricity is met. As the share of wind and solar increases, this will become even more important.    ▪ For offshore development, in particular, the addition of Transmission Access Guarantees (TAGs) is very welcome. This concept aims to mitigate the additional financial risks that offshore hybrid wind farms connected to more than one Member State face when their ability to export electricity is limited by for example onshore grid constraints.    But, of course, this does not mean that now everything is all wrapped up and sorted. For example, TAGs won’t mitigate all financial risks for offshore hybrid projects – here it will be vital to define a detailed framework as soon as possible to ensure there is clarity for upcoming projects.    Still, this is all a positive step for the European energy industry. More steps need to be taken, but we are now heading on the right path.

Modeling something like time series goes past just throwing features in a model. In the world of time series data, each observation is associated with a specific time point, and part of our goal is to harness the power of temporal dependencies. Enter autoregression and lagging -  concepts that taps into the correlation between current and past observations to make forecasts.  At its core, autoregression involves modeling a time series as a function of its previous values. The current value relies on its historical counterparts. To dive a bit deeper, we use lagged values as features to predict the next data point. For instance, in a simple autoregressive model of order 1 (AR(1)), we predict the current value based on the previous value multiplied by a coefficient. The coefficient determines the impact of the past value on the present one only one time period previous. One popular approach that can be used in conjunction with autoregression is the ARIMA (AutoRegressive Integrated Moving Average) model. ARIMA is a powerful time series forecasting method that incorporates autoregression, differencing, and moving average components. It's particularly effective for data with trends and seasonality. ARIMA can be fine-tuned with parameters like the order of autoregression, differencing, and moving average to achieve accurate predictions. When I was building ARIMAs for econometric time series forecasting, in addition to autoregression where you're lagging the whole model, I was also taught to lag the individual economic variables. If I was building a model for energy consumption of residential homes, the number of housing permits each month would be a relevant variable. Although, if there’s a ton of housing permits given in January, you won’t see the actual effect of that until later when the houses are built and people are actually consuming energy! That variable needed to be lagged by several months. Another innovative strategy to enhance time series forecasting is the use of neural networks, particularly Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are designed to handle sequential data like time series. They can learn complex patterns and long-term dependencies within the data, making them powerful tools for autoregressive forecasting. Neural networks are fed with past time steps as inputs to predict future values effectively. In addition to autoregression in neural networks, I also used lagging there too! When I built an hourly model to forecast electric energy consumption, I actually built 24 individual models, one for each hour, and each hour lagged on the previous one. The energy consumption and weather of the previous hour was very important in predicting what would happen in the next forecasting period. (this model was actually used for determining where they should shift electricity during peak load times). Happy forecasting!

Innovation has no boundaries! Who could have imagined this? But these four brilliant Nigerian teenage girls did. At an age when most are focused on exams and friends, they looked at a global problem — energy scarcity — and decided to solve it. They invented a generator that produces six hours of electricity using just one liter of urine. The idea was born when they saw their community struggling with frequent power cuts and expensive, polluting fuels like petrol and diesel. Instead of accepting it as "normal," they asked a simple yet powerful question: "What if waste could be turned into energy?" Through curiosity, science, and teamwork, they turned this question into a life-changing innovation. Why this matters to the world: Millions of people globally still live without reliable electricity. Urine, a freely available resource, could power homes, schools, and hospitals, especially in rural and underdeveloped areas. It can reduce carbon emissions and dependence on non-renewable fuels, creating a cleaner planet. This is more than just an invention — it's a revolution waiting to happen. Imagine a world where something as basic as human waste becomes a sustainable power source, lighting up villages, driving industries, and reducing energy poverty. These girls remind us that innovation isn’t about age, location, or resources — it’s about mindset. When we dare to question, we have the power to change the world. What’s an “impossible idea” you’ve been holding back on? #Innovation #Sustainability #WomenInSTEM #CleanEnergy #Inspiration #GlobalImpact #FutureOfEnergy #Leadership

🔋 How Do Hybrid Solar Systems Keep the Power Flowing – Rain or Shine? 🌞🌧️⚡ Hybrid Solar Systems are more than just rooftop panels — they’re smart energy managers that balance solar generation, battery storage, and grid reliance to ensure uninterrupted power — day 🌅 or night 🌃. ⚙️ Here’s how energy flow works in a Hybrid Solar setup: 🌞 Daytime (High Solar Output): 🔌 Solar powers the connected load 🔋 Excess energy charges the battery 🌐 Surplus is exported to the grid (if applicable) 🌙 Nighttime / Cloudy Weather: 🔋 Battery supplies power to the load ⚡ Low battery? System auto-switches to grid supply 🚫 Grid Outage? 🛡️ Hybrid inverter + battery instantly power critical loads 🎯 Key Benefits at a Glance: ✅ Maximize usage of self-generated solar energy 🔁 Seamless backup during outages 💸 Lower electricity bills & boost grid independence 🔒 Improved reliability for homes & businesses 🌍 Whether you're an engineer designing smart solar systems or simply curious about clean energy tech — understanding Hybrid Solar flow is a game-changer!

An American startup just unlocked hydropower from dry land — with no rivers or dams In a radical rethinking of water-based energy, an American startup has developed a closed-loop hydropower system that works without a natural river or dam. It uses gravity, elevation, and recycled water to generate continuous electricity — even in dry, landlocked regions. This breakthrough could bring the reliability of hydroelectric energy to places never before considered viable. The system works by pumping water to a high-elevation reservoir using solar or wind power during the day. At peak demand or when the sun goes down, the water is released downward through turbines, generating electricity just like traditional hydropower — except it’s all artificial and self-contained. The water is then collected at a lower basin and pumped back up again in a sustainable loop. The beauty lies in its efficiency and controllability: it can store power like a battery and respond instantly to grid needs. Unlike dams, which often flood ecosystems and displace wildlife, this closed-loop design leaves no environmental scar. It doesn’t alter natural rivers, harm fish populations, or require massive civil works. Instead, the units are built on unused land — even deserts — and sized to meet local energy demands. Some are small enough to power rural villages; others are being scaled up for full urban deployment. In testing across Arizona and Nevada, the system has shown round-trip efficiency above 80%, on par with lithium batteries — but with none of the rare earth mining or toxicity risks. Plus, its operational lifespan stretches into decades, making it a stable and low-cost option for long-term storage and energy smoothing. As the world shifts toward renewables, one of the biggest challenges is intermittency — solar and wind don’t always match demand. This artificial hydropower approach could solve that, offering a clean, recyclable way to store energy without heavy infrastructure. It’s not just a battery — it’s water powered engineering, reinvented.

Imagine a discipline in offshore wind farm development that influences revenues over a lifetime: Layout Optimisation!   From a mathematical point of view, the problem that we try to solve is the following: We try to optimise a function f by varying a set of design variables (typically, turbine locations) which are subject to constraints (such as being located in the project area and not being too close to each other).   But what are we optimising for — energy yield, foundation locations, cable costs? All these disciplines must not be optimised in a silo, but need to be considered concurrently to come up with an optimal design. Therefore, we need a KPI able to correctly estimate what should be the tradeoff between construction, operations costs and energy production. The Levelised Cost of Electricity (LCoE) is one of the standard KPIs used within the industry for layout optimisation.   In simple terms, four key factors influence offshore wind farm layouts: ▪️Electrical cabling: Greater distances between turbines increase cabling costs and electrical losses. ▪️Energy yield: Turbines placed too close together suffer higher wake losses. ▪️Foundation costs: Highly dependent on bathymetry. ▪️Logistics costs: Affected by soil conditions and shore distance. So, how do we optimise layouts today? Machine learning is the game changer! With more complex wind farms and 100+ turbines to be placed, optimisation algorithms now handle layouts beyond human capacity.   Next time you spot an offshore wind farm, remember each layout is unique — for a reason!