This service evaluates and optimises advanced control strategies and AI-based algorithms for smart grids, hybrid energy storage systems, and electrical networks. It enables companies to improve energy efficiency, system stability, and operational reliability, supporting the transition towards more flexible and resilient energy systems.
The service assesses customer algorithms in representative scenarios, such as renewable energy integration, load balancing, and hybrid energy storage management. It analyses performance to identify strengths, limitations, and opportunities for improvement, helping companies enhance their control strategies before deployment.
Performance evaluation is based on measurable indicators such as system stability, response time, energy efficiency, power quality, and reliability. These metrics allow companies to quantify improvements in grid performance, reduce energy losses, and optimise resource utilisation.
The service includes:
– Performance analysis and benchmarking of control strategies and algorithms
– Validation in simulation and real-time environments
– Identification of optimisation opportunities
– Proposal and initial evaluation of alternative or complementary solutions (e.g. AI-based predictive control, optimisation algorithms, hybrid strategies)
Evaluations are conducted using advanced testing environments, including digital simulation models and Hardware-in-the-Loop (HIL) and Power Hardware-in-the-Loop (PHIL) platforms. These environments enable realistic testing of electrical systems, including motors, power converters, and electronic loads, under controlled and repeatable conditions.
The service follows a structured process:
– Definition of the use case, system architecture, and key performance indicators (KPIs)
– Integration of customer algorithms into simulation and HIL/PHIL environments
– Execution of test scenarios under varying operating conditions
– Analysis and benchmarking of results
– Development and validation of improved solutions
The result is a pilot application that enables the evaluation of different algorithms for a specific use case and demonstrates the potential of advanced and improved technologies. Expected outcomes include enhanced system stability, improved energy efficiency, faster response times, and optimised control of hybrid energy systems.
Additionally, in the domain of power converters and electrical systems, the service evaluates advanced algorithms for converter control, active filter design, and system management. Testing in HIL and PHIL environments ensures accurate validation of algorithm performance in realistic electrical conditions, supporting the development of more efficient and reliable products.
Typical applications include smart grid management, optimisation of hybrid energy storage systems, control of renewable energy integration, and performance improvement of power electronics. For example, energy system developers can optimise grid stability under variable renewable generation, while manufacturers of power converters can enhance control strategies to improve efficiency and reliability.
To carry out the service, customers are expected to provide access to their control algorithms, system models, and relevant data (e.g. load profiles, generation data). Where applicable, interfaces for integration into HIL/PHIL platforms (e.g. control code, communication protocols) should be available. ITA provides testing infrastructure, simulation environments, and expertise in AI, power systems, and control engineering.
This service is designed for:
– Energy system developers and integrators seeking to optimise smart grid and hybrid energy storage solutions
– Energy storage companies aiming to improve control strategies and system performance
– Manufacturers of power converters and electrical systems looking to validate and enhance advanced control algorithms
To start a project, companies can contact ITA indicating the service reference and application scope. This initial step includes a feasibility assessment, followed by a tailored proposal, execution of validation activities, and delivery of results.
