Resource Optimization

Saving Lakhs Every Month - How I Implemented an AWS Cost Optimization Automation as a DevOps Engineer! When I first joined my current project as an AWS DevOps Engineer, one thing immediately caught my attention: “Our AWS bill was silently bleeding every single day.” Thousands of EC2 instances, unused EBS volumes, idle RDS instances, and most importantly — NO real-time cost monitoring! Nobody had time to manually monitor resources. Nobody had visibility on what was running unnecessarily. Result? Month after month, the bill kept inflating like a balloon. ⸻ I decided to take this as a personal challenge. Instead of another boring “cost optimization checklist,” I built a fully automated cost-saving architecture powered by real-time DevOps + AWS services. Here’s exactly what I implemented: ⸻ The Game-Changing Solution: 1. AWS Config + EventBridge: • I set up Config rules to detect non-compliant resources — like untagged EC2, open ports, idle machines. 2. Lambda Auto-Actions: • Whenever Config detected issues, EventBridge triggered a Lambda function. • This function either auto-tagged, auto-stopped idle instances, or sent immediate alerts. 3. Scheduled Cost Anomaly Detection: • Every night, a Lambda function pulled daily AWS Cost Explorer data. • If any service or account exceeded 10% threshold compared to the weekly average, it triggered Slack + Email alerts. 4. Visibility First, Action Next: • All alerts first came to Slack channels where DevOps and owners could approve actions (like terminating unused resources). 5. Terraform IaC: • Entire solution — Config, EventBridge, Lambda, IAM, SNS — all written in Terraform to ensure version control and easy replication. ⸻ The Impact: • 20% monthly AWS cost reduction within the first 2 months. • Real-time visibility for DevOps and CloudOps teams. • Zero human dependency for basic compliance enforcement. • First-time ever — proactive action before bills got out of hand! ⸻ Key Learning: “Real success in DevOps isn’t just about automation — it’s about understanding business pain points and solving them smartly.” I learned that cost optimization is NOT a “one-time” audit. It needs real-time event-driven systems — combining AWS Config, EventBridge, Lambda, Cost Explorer, and Slack. ⸻ If you’re preparing for DevOps + AWS roles today: Don’t just learn services individually. Learn how to build real-world solutions. Show how you saved time, money, and risk — that’s what companies pay for! ⸻ If you want me to share the full Terraform + Lambda GitHub repo for this cost optimization automation project, Comment below: “COST SAVER” and I will send you the link! Let’s learn. Let’s grow. Let’s solve REAL problems! #DevOps #AWS #CostOptimization #RealTimeAutomation #CloudComputing #LearningByDoing

In a chapter co-authored with Udit Mathur for IDFC Foundation’s India Infrastructure Report 2024, we examine the twin resource challenges shaping India’s clean energy transition: critical minerals and water. As deployment of solar, wind, and storage accelerates, securing access to critical minerals is essential. We outline five strategic priorities for the Government’s Critical Minerals Mission—ranging from long-term planning and exploration to processing capabilities and international partnerships. We also highlight the water risk: India holds just 4% of the world’s freshwater but supports 18% of its population. With renewables expanding in water-scarce regions, we recommend stricter enforcement of water-use norms and cluster-level planning. Our core argument is that with anticipatory policy, institutional reform, and global collaboration, India can deliver on its energy transition goals without being constrained by these vital resources. #EnergyTransition #IIR2024 #ReNewTheFuture Ministry of New and Renewable Energy (MNRE) MoEF&CC

⚛️ Sequential Quantum Computing 📑 We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitations of each type of quantum computer by combining their complementary strengths. Current quantum devices, including analog quantum annealers and digital quantum processors, offer distinct advantages, yet face significant practical constraints when individually used. SQC addresses this by efficient inter-processor transfer of information through bias fields. Consequently, measurement outcomes from one quantum processor are encoded in the initial-state preparation of the subsequent quantum computer. We experimentally validate SQC by solving a combinatorial optimization problem with interactions up to three-body terms. A D-Wave quantum annealer utilizing 678 qubits approximately solves the problem, and an IBM’s 156-qubit digital quantum processor subsequently refines the obtained solutions. This is possible via the digital introduction of non-stoquastic counterdiabatic terms unavailable to the analog quantum annealer. The experiment shows a substantial reduction in computational resources and improvement in the quality of the solution compared to the standalone operations of the individual quantum processors. These results highlight SQC as a powerful and versatile approach for addressing complex combinatorial optimization problems, with potential applications in quantum simulation of many-body systems, quantum chemistry, among others. ℹ️ Romero et al - 2025

Hotspot when Navigating the Energy Transition ! Where is the value in " co-optimizing gas and electricity network planning for decarbonization"??? As energy networks utilities navigate the climate change mitigation policies, Energy system modelers and planners must develop strategies for achieving cost-effective Coordinated planning for electricity and natural gas systems investments that address cross –sector operational constraints, competing demands for net-zero emissions fuels, and shifts in energy consumption patterns. In this context, and In order to rapidly integrate substantial productions from renewable energy sources like - renewable gases and renewable electricity sources- to meet those challenge, it is imperative for electricity and gas network utilities to co-optimize the planning and delivery of network infrastructure, ensuring predictability for customers as they navigate the complex transition to a sustainable energy future. Some Key Components of such effective co-optimization should cover: 1. Effective regulatory frameworks to afford market integration which is vital to create an attractive environment for effective investments. Transparent policies will facilitate the integration of renewable sources while ensuring reliability and affordability for consumers. 2. crucial and pivotal roles of "elec., gas" Transmission System Operators (TSOs) and Distribution System Operators (DSOs) must be coherent and aligned to collaboratively enhance capacity management. This synergy will optimize the flow of energy, accommodate fluctuating renewable generation, and maintain both grids dispatchability and stability. 3. increasing the renewable energy production capacity, makes managing this influx is crucial. therefore, Strategic co-optimized modeling and planning of both energy grids will ensure stable handling of peak loads and diverse energy sources without compromising service reliability. 4. Tariff Structures: Evolving inclusive tariff structures will play a significant role in incentivizing investments in both gas and electricity networks. Fair pricing mechanisms are essential to stimulate growth while promoting sustainable energy practices. 5. Investment Planning: Coordinated investment planning across gas and electricity sectors is critical. Prioritizing infrastructure projects that enhance integration and resilience will pave the way for a more robust energy affordability. 6. The Role of Hydrogen and Power-to-X (PTX): Hydrogen and PTX technologies represent a promising avenue for energy transition by leveraging adoption of such solutions to store excess renewable energy and provide flexibility to energy systems, as well as effectively contribute to decarbonization efforts. Indeed …co-optimizing gas and electricity network infrastructure is a critical and strategic job! #EnergyTransition #Decarbonization  #RenewableEnergy #Hydrogen #MarketRegulation #CapacityManagement #InvestmentPlanning

Just slashed a client's dev environment costs by 64% using AWS CDK and EventBridge Scheduler. The solution? 50 lines of core logic, zero maintenance overhead. Here's the breakdown: Their dev environment was running 24/7 – a common oversight I see in many AWS setups. Multiple RDS instances and EC2 servers were consuming resources during off-hours, essentially burning money while developers sleep. The solution leverages AWS EventBridge Scheduler with AWS CDK for infrastructure as code: - Automated start/stop schedules for RDS and EC2 instances (weekdays 7 AM - 7 PM) - IAM roles and permissions handled through CDK constructs - Dead Letter Queue for failed operations monitoring - Timezone-aware scheduling (critical for distributed teams) - Zero manual intervention needed after deployment The real power isn't just in the cost savings – it's in the maintainability. One CDK construct can manage multiple instances, and adding new resources is as simple as updating an array of identifiers. Key metrics: - 108 hours/week reduction in runtime - 64% reduction in dev environment costs - Resource utilization aligned with actual working hours - 10-minute deployment time - ROI from day one Are you still running your dev instances 24/7? #AWS #CloudCost #IaC #DevOps #AWSCDK #CostOptimization

A Battery Energy Storage System (BESS) site survey is a crucial step before designing and deploying a BESS project. 1. Site Location and Accessibility ✅ Geographical Coordinates – Latitude & longitude of the site ✅ Site Access – Road conditions, distance from the main highway, transport feasibility ✅ Security – Fencing, surveillance, and access control requirements ✅ Environmental Conditions – Nearby water bodies, forests, flood zones 2. Electrical Infrastructure ✅ Grid Connection – Distance from the nearest substation, voltage levels, and grid capacity ✅ Existing Transformers & Switchgear – Availability, ratings, and need for upgrades ✅ Point of Interconnection (POI) – Location, capacity, and grid compliance requirements ✅ Power Quality Parameters – Voltage fluctuations, harmonics, and frequency variations 3. Load Profile & Energy Needs ✅ Peak Demand (MW/MWh) – Maximum and minimum load requirements ✅ Load Fluctuations – Seasonal variations and power demand curve ✅ Backup Requirements – Grid support, peak shaving, or islanding capability ✅ Future Load Expansion – Provision for additional capacity 4. Environmental & Climatic Conditions ✅ Temperature Range – Min/max temperature for BESS thermal management ✅ Humidity & Rainfall – Impact on enclosures, electrical components, and corrosion risk ✅ Seismic & Wind Load – Structural stability against earthquakes and storms ✅ Flooding Risk – Historical flood data, drainage facilities, and mitigation measures 5. Space & Layout Considerations ✅ Available Land Area – Space for BESS containers, transformers, and switchgear ✅ Ground Conditions – Soil testing, load-bearing capacity, and need for reinforcement ✅ Shading & Heat Islands – Impact of nearby structures on ventilation and cooling ✅ Fire Safety Clearances – Minimum spacing for fire protection and emergency access 6. Safety & Compliance ✅ Fire Suppression System – Availability of fire detection, suppression (e.g., FM-200, NOVEC) ✅ Local Regulations & Permits – Compliance with electricity board and environmental laws ✅ Battery Safety Standards – IEC 62619, UL 9540A, NFPA 855, and other applicable standards ✅ Hazardous Material Handling – Battery electrolyte safety and emergency handling procedures 7. Communication & Control Systems ✅ SCADA & Monitoring – Remote access, data logging, and integration with grid operations ✅ Internet Connectivity – Availability of fiber, cellular, or satellite communication ✅ Cybersecurity – Protection against hacking, data security protocols ✅ Telemetry & Alarms – Real-time alerts for temperature, SOC, SOH, and fault conditions 8. Civil & Structural Requirements ✅ Foundation Type – Concrete pad, piles, or elevated structures based on soil study ✅ Drainage & Water Management – Preventing water accumulation near battery enclosures ✅ Cable Routing & Trenching – Underground or overhead cabling for power and communication ✅ Cooling System Installation – HVAC or liquid cooling provisions

In power system engineering, transformer efficiency is not merely a performance metric—it is a pivotal factor in lifecycle cost analysis, load planning, and energy loss mitigation. The maximum efficiency point (MEP) of a transformer is achieved when the variable (load-dependent) losses equal the constant (core) losses. ⚡ Engineering Implications: 🔹 Design Stage: Transformer designers target this MEP to coincide with the expected load profile—often 60%–70% of rated capacity for distribution transformers. 🔹 Operational Stage: Energy auditors and distribution planners can determine if a transformer is under- or over-utilized by analyzing real-time loading vs. its MEP, improving all-day efficiency and loss allocation. 🔹 Asset Optimization: Accurate estimation of the MEP helps in transformer sizing during network planning, thereby ensuring cost-effective deployment and longevity. 📊 Practical Note: In distribution systems, all-day efficiency often takes precedence over peak efficiency, especially where the load fluctuates significantly over 24 hours. Hence, locating the MEP at or near the average load ensures better energy conservation across time. As the grid modernizes with increasing DER penetration and dynamic loads, precise transformer loading strategies grounded in efficiency analytics become critical. Transformer efficiency is no longer static—it’s a dynamic performance vector shaped by real-world loading and operational philosophy. #TransformerDesign #PowerDistribution #EfficiencyOptimization #EnergyLossReduction #ElectricalEngineering #SmartGrid #AssetManagement #AllDayEfficiency #TransformerEfficiency #LoadProfile #PowerSystems

Context is everything, in life AND in SEO. There’s an old joke: A student calls home and says, “Mum, I crashed the car, dropped out of uni, and I’m pregnant.” (Pause) “Just kidding, I just failed an exam.” Suddenly, failing an exam doesn’t seem that bad given the previous context. SEO is a lot like that. You might say: “We have to switch to server-side rendering, it’s better for SEO!” But then you learn: - It’ll delay product launches by 3 months - It costs £60k in dev time - Google already renders the JS just fine - The real issues are poor internal linking and content quality Now, that “must-have” starts looking like a “nice-to-have”. 💡 The more you understand how systems actually function, and what they cost, the better your recommendations become. Here are 5 ways you can level up your prioritisation skills: 🧠 1. Talk to engineering regularly Sit in on sprint planning or dev stand-ups. Ask how long certain changes really take, what’s blocked, and what other teams are prioritising. 💷 2. Ask: “What’s the opportunity cost?” Before pushing a fix, ask: “What won’t get done if we do this instead?” This forces you to consider business impact, resource trade-offs, and urgency in a broader context. 🧾 3. Learn the basics of cost modelling If a change requires design/dev time, estimate the effort and multiply it by an hourly internal rate or contractor cost. Even rough numbers can help you decide what’s really “worth it”. 📊 4. Get comfortable with business metrics Understand how SEO ladders up to revenue, leads, or other KPIs. If a rec isn’t clearly linked to impact, it’s harder to justify when budgets are tight. 🔁 5. Shadow other departments Spend more time observing how product managers, analysts, or commercial teams make decisions. You’ll see how SEO fits into the bigger picture and how to pitch your ideas in their language. Not everything you can do is worth doing.

What happens when more than 28 GW of the ERCOT fleet goes out on extended maintenance at the same time, including a large number of the fast-ramping gas peaking units? Nothing. When you have 10 GW of dispatchable energy storage to back it up. Today, Net Load + Thermal Outages peaked at 71.7 GW! Fortunately, there is now ~10 GW of battery energy storage operating on the grid on a daily basis. Peak dispatch today of these units was 4.5 GW, which at the time was 9.7% of all dispatching units and of net load requirements. Up from 0% 24 months ago. Why only 4.5 GW out of 10 GW? Because as a group they deployed for many hours, optimized based on traders and AI algorithms. In addition, there were another ~4-5 GW of battery storage units in the ancillary markets. Why does this matter? Because having instant-ramping battery energy storage backing up the system in the ancillary markets helps CREATE additional gas capacity by ensuring that gas units can participate in the energy market and not just sit in reserve. Ancillary markets are critical for the safe operation of the grid by providing instant backups when unforeseen events occur. By allowing technology-appropriate optimization of the whole system and battery storage to provide backup services, new gas capacity has been created that now is able to focus on energy markets during tight days like today. Today is the epitome of what actual resource adequacy (RA) and Reliability looks like, and highlights the negligent approaches to RA and Reliability that classical approaches like rote ELCC calculations wantonly avoid. Today also highlights why it would be sheer lunacy to use DRRS as a reliability product without the inclusion of 4-hour battery energy storage, given that the thermal fleet is a massively-correlated systemic risk to the system during the periods when it must take planned outages for maintenance. Reliability and Resource Adequacy must be defined by clear and objective technical operating criteria to promote technological innovation and the addition of new uncorrelated resource types - rather than discriminating against specific resources. When the thermal fleets have to go on maintenance, the power still needs to flow. If market participants hadn't built 10 GW of battery energy storage in 36 months, then 4.5 GW wouldn't have been available at the peak moment of net load, and the PRC would have dropped from ~7 GW down to 2.5 GW. Today would have been an EEA 2 event with prices at the market cap of $5000/MWh without the billions of dollars invested in the construction of battery energy storage built in the last 36 months. #ERCOT #ResourceAdequacy #DispatchablePower #Powermarketdesign #energystorage

Optimising Electrolyser Investments for Green Hydrogen Production: Green hydrogen is a critical pillar of the global energy transition. Yet, optimising investments in electrolysers is far from straightforward. Market volatility, high CAPEX, and uncertain hydrogen demand make investment timing, sizing, and utilisation complex. Here are my insights, backed by recent studies and practical experience: 🟢 Electricity prices: ~€80/MWh | Hydrogen prices: ~€100/MWh | Electrolyser CAPEX: €1.2M/MW. 🟢 Curtailment rates rising: Chile (2–6%), Germany (1–8%). 🟢 Hydrogen production to avoid curtailment alone yields low ROI. 🟢 Strategic market trading multiplies project value by >10x. 🟢 Hydrogen storage improves revenues by ~10%. 🟢 Optimal electrolyser capacity: 6–10 MW for a 50 MW wind farm. 🟢 Electrolyzer efficiency ~65%; critical for profitability. 🟢 Optimal investment timing: 4–7 years from project start. 🟢 Dynamic decisions boost NPV by 15–20% over static strategies. 🟢 Storage CAPEX: €30k/MWh; OPEX ~2% of CAPEX. 🟢 Key Decision Drivers: 🟩 Hydrogen price volatility: ±20% 🟩 CAPEX trends declining 1% p.a. 🟩 High renewable capacity factors (>0.4) favor investments. 🟩 Hydrogen storage boosts arbitrage opportunities during volatile prices. 🟢 Market Outlook: 🟩 Global electrolyser capacity to exceed 450 GW (2030), 1700 GW (2040). 🟩 Hydrogen demand CAGR >5% till 2050. 🟩 Storage + trading adds €3–4 million extra lifetime revenue. 🟢 Recommended Approaches: 🟩 Use real option models, not static IRR analyses. 🟩 Optimise electrolyser sizing dynamically, between 4–10 MW. 🟩 Consider storage only where price volatility >25%. 🟩 Watch for hydrogen subsidy schemes and carbon markets. 🟩 Integrate Approximate Dynamic Programming (ADP) in planning tools. 🟢 Operational Insights: 🟩 Hydrogen sales replace lost electricity sales during negative price events. 🟩 Hydrogen OPEX is modest (~€1,200/MW/year), while CAPEX dominates. 🟩 Trading electricity and hydrogen boosts revenues by €10+ million over 10 years. ✳️ Electrolyser investments are no longer just a hedge against curtailment. They are dynamic market assets. Optimising their deployment through flexible, market-aware strategies unlocks significant value. Let’s drive the #HydrogenEconomy with smart, data-driven investments. #GreenHydrogen #ElectrolyzerInvestments #RenewableEnergy #EnergyTransition