Home Energy Management System Validation in Real Time (HEMSVRT)

The proposed project focuses on enhancing the operational efficiency and self-sufficiency 
of micro-Energy Hubs (mEH) through strategic management and real-time operational plan
ning. The primary aim is to optimise the performance of each mEH by carefully managing the 
various assets based on a detailed analysis of their flexibility, usually done by a HEMS. This 
involves developing and implementing a tailored energy efficiency strategy that aligns with the 
specific capabilities and potential of each asset within the micro hubs. 
The first objective of the project is to increase the efficiency of the mEHs. By employing a 
strategic operational approach that leverages the estimated flexibility of different assets, the 
efficiency of the mEHs can be maximized. This strategy involves adjusting the operational 
parameters of each asset to ensure that they are functioning at their optimal capacity, thereby 
reducing wastage and improving overall energy output. The careful management of assets 
based on detailed flexibility analysis will lead to the development of tailored energy efficiency 
strategies. These strategies are designed to align with the specific capabilities and potential of 
each asset within the micro hubs, ensuring that all resources are utilized in the most efficient 
manner possible.  
The second objective is to enhance the self-sufficiency of the mEHs. This is achieved 
through a real-time operational plan that dynamically adjusts to the energy production and 
consumption patterns of the hub. By continuously monitoring and adjusting operations in real
time, the reliance on imported energy can be minimized. This contributes to greater energy 
independence at the local level, allowing the micro hubs to operate more autonomously and 
sustainably. Enhancing self-sufficiency not only reduces dependency on external energy 
sources but also ensures a more stable and reliable energy supply within the hubs.  
To measure the effectiveness of the proposed strategies, several key performance indica
tors have been identified. These include the reduction in primary energy demand and con
sumption, the amount of flexible energy unlocked, the increase in self-sufficiency, and the re
duction in total annual costs for the Integrated Local Energy Community. Additionally, the pro
ject aims to achieve a reduction in daily and annual CO2 emissions, leading to significant en
vironmental benefits. Energy savings for consumers are also a crucial KPI, as more efficient 
energy management will result in lower energy bills for end-users. Furthermore, the project 
seeks to increase the penetration of renewable energy sources in the local generation mix, 
promoting the use of cleaner energy alternatives. Finally, the internal rate of return for the 
energy systems deployed in the mEH/EH will be calculated to assess the economic viability 
and profitability of the implemented strategies. 
The proposed project involves several key actors. First, the end users, including both con
sumers and active customers, are central to the project's implementation as they utilize and 
interact with the energy produced by the micro energy hubs. Energy Service Companies pro
vide essential services such as energy efficiency improvements, system optimization, and on
going maintenance, ensuring the mEHs operate efficiently. Technology suppliers deliver the 
advanced hardware and software necessary for smart grid operations and real-time energy 
management. Energy storage owners contribute by providing storage solutions that balance 
supply and demand, enhancing the system's reliability. Additionally, electric vehicle owners 
play a role by integrating their vehicles into the energy network, offering flexible storage options 
and contributing to demand response strategies. 
The successful experiment relies on the integration of several key technologies. These 
technologies are crucial for enhancing the flexibility, efficiency, and self-sufficiency of energy 
systems. The integration of renewable energy sources such as PV panels and solar thermal 
systems is fundamental to generating clean, sustainable energy locally. Energy storage sys
tems, including BESSs, thermal storage systems, Electric Vehicles (EVs), and hydrogen stor
age systems, are essential for balancing supply and demand, storing excess energy, and 
providing backup power. Additionally, energy conversion technologies, such as micro-Com
bined Heat and Power (mCHP) units, fuel cells, and water electrolysers, play a major role in 
efficiently converting energy into usable forms, thereby optimizing the overall performance of 
the mEHs.  
To understand which parameters should be measured, several Key Performance Indices 
(KPIs) are identified:  
• Reduction in primary energy demand and consumption 
• Flexible energy unlocked 
• Increase in self-sufficiency 
• Reduction in total annual cost for the Integrated Local Energy Community (ILEC) 
• Energy savings for the consumers 
• Increase of penetration of renewable energy sources (RES) in the local generation mix 
The experimental results provide a comprehensive analysis of the performance and inter
actions of the Simulink model with a cloud-based optimization solver, evaluating both Hard
ware-in-the-Loop (HIL) and Power-Hardware-in-the-Loop (PHIL) setups. The Simulink model 
effectively mirrors the thermal and electrical profiles of the cloud-based solver, demonstrating 
a strong correlation in load and generation profiles, battery state of charge (SoC), and com
bined heat and power (CHP) outputs. Minor deviations in power and efficiency were attributed 
to differences in model assumptions, such as battery efficiency and specific operating condi
tions, which did not significantly impact the overall consistency between models. 
The PHIL analysis required adjustments in time discretization to maintain real-time condi
tions, highlighting computational constraints in the OPAL-RT system, especially with thermal 
models. To mitigate this, certain thermal models were excluded, allowing the experiment to 
run closer to real-time. Despite some ripple and minor desynchronization in current signals 
affecting instantaneous power, the models remained largely synchronized, achieving the ex
perimental objectives within an accelerated simulation window. 
The experiment successfully integrated the real-time power profiles, control signals, and 
physical equipment for each micro-energy hub (mEH) into the model, achieving accurate data 
representation and load tracking. Thermal energy exchange with the District Heating Network 
(DHN) and solar thermal outputs also demonstrated high fidelity, accurately reflecting cloud
based data. The Battery Energy Storage System (BESS) showcased reactive SoC manage
ment, closely following the reference power. The limitations imposed on natural gas for the 
CHP systems validated model responsiveness to constraints, while voltage and current moni
toring maintained stability, essential for evaluating distributed energy resource impacts within 
a medium-voltage (MV) network. The experiment confirms the model's robustness and pro
vides insights into real-time distributed energy resource management in integrated thermal and 
electrical grids.