Gadgets

Groundbreaking Research on the Architecture of Smart Cities: Balancing Energy, Comfort, and Financial Gains

by Uncategorized

Groundbreaking Research on the Architecture of Smart Cities: Balancing Energy, Comfort, and Financial Gains.

Smart City Architecture

Smart City Architecture

Background:

The transition from smart homes to smart cities brings forth challenges such as unstable power generation, inefficient demand-side integration, low financial returns, and insufficient communication among agents. Overcoming these hurdles using innovative solutions is key to optimizing energy use, improving comfort, and enabling sustainable financial outcomes through efficient energy trading in urban areas.
You can read more about technological advancements toward smart energy management in smart cities here

Solution:

Our energy scientists and analysts developed a cutting-edge four-stage model addressing these challenges with advanced algorithms and smart infrastructure:

Stage 1:

Integrated Smart Hybrid Models (LSTM + GNN) and Graph Neural Networks (GNN) for enhanced load forecasting. This improved forecasting accuracy, achieving a MAPE of 0.0776 for the Smart Hybrid Model, surpassing the LSTM model’s MAPE of 0.0787 by 1.14%.

Stage 2:

We developed a smart home system with interconnected devices and adaptive thermostats designed to manage electricity use efficiently and intuitively. Leveraging MOPnP (Multiobjective Plug-and-Play) and MOPPnP (Multiobjective Prioritized Plug-and-Play) algorithms, this smart home system strikes the ideal balance between user comfort and energy efficiency.

 

Stage 3:

Developed scalable smart city infrastructure to analyze high-RES and low-RES penetration scenarios. Our intelligent MMG structures reduced costs, improved energy usage, and created a strong foundation for smart energy trading in diverse city environments.

Stage 4:

Implemented decentralized energy trading setups, yielding unrealized PnL rates of +116%, +78%, +71.63%, and +250%, supporting high ROI, efficient payback periods, and profitable stakeholder engagement.

Results:

  • Forecasting Accuracy: MAPE improved to 7.76%, outperforming the baseline by 1.14%.
  • Cost Efficiency: Reduced monthly electricity expenses by up to 94%.

Energy Trading Gains: Achieved unrealized PnL rates of +116%, +78%, +71.63%, and +250%.

This groundbreaking work shows how advanced forecasting models, smart home systems, and decentralized energy trading can shape future cities that are sustainable, efficient, and highly profitable.To see how our consumer-ready products bring these innovations to life, explore our solutions here.

Energy Trading

Load Forecast (30 days)

Energy Trading

Complete Setup Results

Research on climate change

Research on Climate Change Impact Assessment Using ANN and Regression-Based Models for Weak Transmission Grids

by Uncategorized

Climate Impact on Grids: ANN & Regression Models for Energy-Efficient Solutions

thermal analysis

Increase in demand in selected planning regions (a) 2050, RCP 45 (b) 2050, RCP 85 (c) 2080, RCP 45 (d) 2080, RCP 85

Background:

Climate change is significantly impacting global energy systems, especially in regions with weak transmission grids. This study explores how artificial neural networks (ANN) and regression models can be leveraged to assess the impact of climate scenarios on electricity demand and infrastructure performance. By using energy forecasting techniques and analyzing long-term climate data, this research provides insights into how future climate variability might affect grid stability and energy planning. Additionally, it highlights the role of energy-efficient solutions in adapting to climate-induced challenges, ensuring that energy systems remain sustainable and resilient in the face of changing environmental conditions.

Solution:

Our energy scientists developed an ANN and regression-based framework to quantify climate impacts and offer actionable insights:

1. Demand Forecasting:

ANN and Quadratic Regression models analyzed historical demand patterns and climate projections, achieving forecasting efficiencies of 92.42% (PESCO), 92.02% (LESCO), and 91.98% (IESCO). These models projected significant demand increases in hotter regions, emphasizing the need for targeted planning.

2. Transmission Line Thermal Analysis:

Thermal modeling revealed a potential 23.34% reduction in transmission capacity due to rising temperatures. This highlights the necessity of upgrading transmission infrastructure with temperature-resistant cables.

3. Renewable Energy Integration:

Comparative analysis of thermal and solar PV systems underscored the resilience of renewables, advocating their increased adoption to meet energy efficiency and climate goals (aligned with SDG-7 and SDG-13).

4. Climate Scenarios and Mitigation:

Scenario analysis explored optimistic (RCP 45) and worst-case climate scenarios, recommending smart grid technologies, energy-efficient solutions, and infrastructure improvements to mitigate adverse impacts.



Results:

  • Demand Projections: Sharp increases in peak demand, particularly in hotter regions.
  • Transmission Impact: Up to 23.34% capacity loss in existing infrastructure under extreme conditions.
  • Model Performance: Robust forecasting models with over 92% accuracy in key regions.
  • Policy Recommendations: Includes renewable integration, smart grid technologies, and enhanced transmission systems.

This research demonstrates how advanced analytics can assess and address climate-related challenges, ensuring resilient and sustainable energy systems in developing nations. Here are some of the Energy-Efficient Solutions developed by our team.

Energy-Efficient Solutions

Distribution of percentage Change in Ampacities for 500 kV and 220 kV Transmission Lines.

energy efficient solutions

Percentage decrease in overall thermal power output across the years under RCP 45 and RCP 85

mcg

Research on Intelligent Multi-Stage Optimization for Community-Based Microgrids

by Uncategorized

Case Studies on Multi-Agent Systems for Microgrid Control and Optimization: Intelligent Solutions for Community-Based Microgrids

Case studies on multi-agent systems for microgrid control and optimization showing communication structure between layers in a multi-microgrid setup.

Proposed Multi layered multi agent Network for smart connected community within a Multi-Microgrid

Background:

In the case studies on multi-agent systems for microgrid control and optimization like power instability, intermittent load integration, and inefficient communication. These issues become more complex when multiple decentralized units must coordinate effectively. This blog explores case studies on multi-agent systems for microgrid control, showcasing how intelligent, multi-layered frameworks can solve these issues using real-time decision-making and energy optimization.

Solution:

In the case studies on multi-agent systems for microgrid control and optimization.Our research proposes an intelligent, multi-agent-based system (MAS) with advanced algorithms to optimize power, price, and comfort across hierarchical community microgrid structures:

1.Multi-Layered Control Framework for Microgrid Systems:

We present a hierarchical architecture for multi-microgrid (MMG) environments using home, local, and global controllers. This structure leverages multi-agent systems (MAS) to process real-time (RT) and day-ahead (DA) signals. As demonstrated in our case studies, this ensures seamless coordination between energy resources, consumers, and grid components.

2. Implementation of Intelligent Algorithms Based on Specific Scenarios

Algorithms like Prioritized Plug and Play (PPnP), Prioritized Particle Swarm Optimization (P-PSO), and Knapsack were used in Python and MATLAB (PADE framework). These were tested in simulated community setups. Each case study illustrates how MAS improves power dispatch, optimizes pricing models, and maintains comfort levels.

3. Smart Devices and Real-Time Signal Integration:

Smart thermostats and HVAC control systems were integrated into each test scenario. RT and DA signals were used to dynamically adjust performance based on consumer needs. The multi-agent system effectively ensured energy savings and consistent user satisfaction in each case study.

Results:

  • RES Integration: These results from the case studies on multi-agent systems for microgrid control and optimization show that  Renewable energy generation improved by 1.4% in Case 2 and 1.9% in Case 3, illustrating the benefits of renewable integration in community microgrids.
  • Cost and Comfort Balance: Cost and Comfort Balance: High-income households experienced minimal price increases (1.0064% in Case 2, 1.1154% in Case 3) while maintaining optimal comfort levels, demonstrating the system's ability to balance cost-efficiency with user satisfaction.
  • Efficiency Gains: Power dispatch efficiency improved by 1.2564% in Case 2 and 1.7939% in Case 3, compared to the baseline, showcasing the optimization potential of multi-agent systems for microgrid control.
  • Practical Scenarios: Our case studies demonstrated the effectiveness of the MAS approach in addressing community microgrid challenges, including energy optimization and user comfort.

Energy management in microgrids is a growing field. For instance, Renewable Energy World discusses the importance of renewable integration in modern microgrids, noting that improvements in optimization can increase energy efficiency.
Explore more on renewable integration

Impact:

This innovative framework enhances communication between microgrid layers, ensures optimal resource utilization, and balances cost-efficiency with user comfort. It provides a scalable, intelligent solution for modern energy systems within community-based microgrids.
To learn more about energy research click here.

Screenshot 2024-12-06 214951

Communication structure between different layers in python agent development in multi-microgrid setup.

cmh 2

Research on Climate Change Impacts and Mitigation Strategies for Domestic Electrical Grids

by Uncategorized

Smart Grid Solutions for Enhancing Climate Resilience in Domestic Electrical Grids.

Flowchart of integrated quantification for climate impact analysis and smart grid solutions in domestic electrical grids.

Integrated Quantification Approach

Quantitative Framework:

The IQA incorporates historical data extrapolation, real-time analysis, and thermal modeling (TMA) to project future GHG reduction and renewable energy trends. It integrates EEDs into domestic load profiles through plug-and-play algorithms, optimizing energy consumption while enhancing user comfort. Comparative analyses of policies for developed and developing nations highlight the significant potential for emissions curtailment through smart grid solutions in Pakistan.

Background:

Climate change poses significant challenges to the energy sector, particularly in developing nations where electrical grids are major sources of greenhouse gas (GHG) emissions. According to Columbia University, climate change can also directly affect the efficiency and availability of renewable energy sources .To address these challenges, this study introduces an Integrated Quantification Approach (IQA) ) that leverages smart grid solutions tailored for developing countries, emphasizing renewable energy integration, demand-side management (DSM), and energy-efficient devices (EEDs). By evaluating the impacts of ambient temperature increases (0.5 °C to 2 °C) on thermal power generation and transformers, this framework provides actionable insights into reducing emissions and improving grid efficiency.

Results and Impacts:

The share of thermal energy in the mix decreases from 68.43% to 40.2% by 2030, with CO2 emissions dropping by 55% compared to the base case.

Transmission and generation losses due to climate impacts are quantified, with transformer losses rising from 180 MW to 1770 MW and thermal plant losses reaching 1440 MW by 2030.

EED adoption reduces average domestic load by 1250 MW, saving 10.7% in energy consumption and lowering unit costs by 12.8% by 2030.

Conclusion:

This study underscores the necessity of indigenous R&D for DSM and renewable energy options to combat climate impacts. Policymakers are encouraged to adopt dedicated frameworks, improve tax incentives, and promote local EED manufacturing. Future work will expand to assess climate impacts on industrial and agricultural sectors and provide detailed socio-economic analyses to strengthen climate resilience strategies.
To learn more about renewable energy solutions click here

Screenshot 2024-12-06 215830

Transmission and Distribution (T&D) losses for Vision 2025, NSEP and Base Case

Screenshot 2024-12-06 215845

Thermal plant behavior with respect to ambient temperature for different case scenarios (till year 2030)

cmh 2

template 2

by Uncategorized

Research on Climate Change Impacts and Mitigation Strategies for Domestic Electrical Grids

Background:

Climate change poses significant challenges to the energy sector, particularly in developing nations where electrical grids are major sources of greenhouse gas (GHG) emissions. To address these challenges, this study introduces an Integrated Quantification Approach (IQA) tailored for developing countries, emphasizing renewable energy integration, demand-side management (DSM), and energy-efficient devices (EEDs). By evaluating the impacts of ambient temperature increases (0.5 °C to 2 °C) on thermal power generation and transformers, this framework provides actionable insights into reducing emissions and improving grid efficiency.

Quantitative Framework:

The IQA incorporates historical data extrapolation, real-time analysis, and thermal modeling (TMA) to project future GHG reduction and renewable energy trends. It integrates EEDs into domestic load profiles through plug-and-play algorithms, optimizing energy consumption while enhancing user comfort. Comparative analyses of policies for developed and developing nations highlight the significant potential for emissions curtailment in Pakistan.

Flowchart of integrated quantification for climate impact analysis and smart grid solutions in domestic electrical grids.

Results and Impacts:

The share of thermal energy in the mix decreases from 68.43% to 40.2% by 2030, with CO2 emissions dropping by 55% compared to the base case.

Transmission and generation losses due to climate impacts are quantified, with transformer losses rising from 180 MW to 1770 MW and thermal plant losses reaching 1440 MW by 2030.

EED adoption reduces average domestic load by 1250 MW, saving 10.7% in energy consumption and lowering unit costs by 12.8% by 2030.

Screenshot 2024-12-06 215830

Conclusion:

This study underscores the necessity of indigenous R&D for DSM and renewable energy options to combat climate impacts. Policymakers are encouraged to adopt dedicated frameworks, improve tax incentives, and promote local EED manufacturing. Future work will expand to assess climate impacts on industrial and agricultural sectors and provide detailed socio-economic analyses to strengthen climate resilience strategies.

 

Screenshot 2024-12-06 215845