Case Study

Smart Grid

Customer(EU): Global energy company, bringing energy to people through the adoption of new sustainability-oriented technologies.

Challenges

Integration of IoT technologies into the existing electrical power grid infrastructure to enhance its efficiency, reliability, and sustainability.

Real-time grid monitoring & control complexity – Implementing an IoT-based electrical grid for real-time monitoring, control and management of energy distribution and consumption.
Data collection & analytics across heterogeneous devices – Handling large volumes of consumption data, distributed sensors, and diverse endpoints for the grid.
Reliable connectivity and system integration – Ensuring all grid-elements and IoT devices reliably communicate with backend systems under varying conditions.
Scalability & infrastructure adaptation – Adjusting existing electrical distribution infrastructure to support “smart” features while keeping performance and cost acceptable.
Security & resilience in grid operations – As the grid becomes more data-driven and connected, ensuring resilience against faults, cyber threats, and ensuring operational reliability.

Solution

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IoT-based grid architecture – Deploying IoT sensors and communication infrastructure to capture real-time data and enable grid control functions.

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Cloud/edge backend for data processing – Utilizing cloud or edge systems to aggregate, process, and analyze grid data for actionable insights.

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Multi-protocol network & sensor integration – Connecting sensors, meters, actuators across different protocols to unify into the smart grid framework.

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Scalable modular deployment model – Rolling out grid upgrades in phases, enabling incremental adoption without full system overhaul.

Key Impact

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Improved grid visibility & control – Operators gain real-time insights into energy flows, consumption patterns, and distribution health.

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Better energy management & efficiency – With more granular data, the grid can optimise loads, reduce waste, and improve cost efficiency.

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Enhanced customer service & reliability – Fewer outages, faster responses, and improved reliability for the end-users.

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Future-proofing infrastructure – The smart grid foundation supports future innovations (e.g., renewable integration, demand response, EV charging).

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Operational cost savings & scalability – Modular deployments and IoT-enabled management reduce long-term operational overhead and support growth.

Applied Methodology

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Requirements gathering & system architecture definition – Establishing functional, non-functional and data requirements for the smart grid IoT system.

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Hardware/IoT sensor deployment and communication setup – Installing sensors/meters, setting up communication infrastructure, and integrating endpoints.

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Backend system development (cloud/edge) and data pipeline – Building data ingestion, processing and analytics systems to support smart-grid functionalities.

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Pilot deployment and iterative rollout – Starting with a limited deployment, testing functionality, then iteratively scaling to full grid coverage.

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Monitoring, verification & continuous improvement – Establishing operational monitoring, validating performance against KPIs, refining deployment as more data is collected.

Tasks / Responsibilities

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Software Requirements

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Software Architecture

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Detailed design

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Code implementation (Embedded C/C++, Flutter, Python)

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Unit Test

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Static / Dynamic analysis

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Traceability

Toolchain / Technologies

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WiFi

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BLE

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Zigbee

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Flutter

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AWS

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Sensors & Actuators

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Smart meters

Team Composition

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1 System engineer

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1 Software Architect

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1 Data Engineer

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1 Embedded Engineer

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1 Flutter Engineer

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2 Full Stack Developer

First delivery

%

Real time data

%

Customer Satisfaction

Helping OEMs Grow with Technology

By driving innovation, enhancing operational efficiency, fostering long-term partnerships, and providing training and education, CONCEPTHYPE makes a significant impact on OEMs’ success.