Collaborative Microgrids as a Mitigation Mechanism to Renewable Energy Intermittence in the Transition to Sustainable Energy

Abstract

The volatility of renewable energy outputs is a well-known obstacle that has hindered the integration of more renewables in the UK’s energy portfolio. The contemporary energy systems relying on renewables such as wind and solar must handle a double-sided variability from demand uncertainty and the variability of weather conditions. Microgrids (MGs) may function as an effective means of integrating more renewables, particularly if they can effectively control the volatility of renewables at a smaller scale (the MG level) through a collaborative operating strategy with the utility grid. Improving the communication strategy between MGs and the utility grid facilitates enhanced controllability and coordination of power system networks.
The aim of this PhD work is to analyse energy systems via the lens of supply chain management. In particular by contemplating enhancements to the balancing mechanism between the utility grid and MGs. Advocating for a transition from selfish MGs to collaborative MGs that manage their volatile unpredictable demand given to the utility grid. A novel operational strategy for the orders given from the MG to the utility grid called precontracted order updates (COU) is designed after level production principles in lean thinking. In this thesis COU operational strategy distinguishes the collaborative MG that employs the COU strategy from the selfish MG utilises the conventional spot order update (SOU) strategy.
This research utilised a mathematical modelling approach and real-life weather data to define the COU strategy and simulate the selfish and collaborative MG models. In order to investigate the efficiency of the collaborative MG system compared to the selfish MG, this research is organized into four articles. The first article investigated the efficiency of the collaborative system in absorbing the unpredictable volatile demand given to the utility grid from the MG as well as defining the best COU scenario that has ultimate performance. The results indicate that the implementation of the COU strategy in the collaborative MG mitigates unpredictable volatility (SOU) ordered to the utility grid compared to the selfish MG. The 24-step (planned volatile demand) scenario of the COU exhibits superior performance compared to all other COU scenarios examined. The second article evaluates the collaborative MGs from the perspectives of sustainability and carbon emissions, considering different backup generation options in both MG models (selfish and collaborative). Findings reveal that COU strategy shifted the collaborative MGs towards reduced unplanned order volatility (SOU) and lower carbon emissions in power supply, particularly when the hydrogen burner was utilized as the backup generation option in the collaborative MG model. The third article examines the energy cost implications of collaborative MGs compared to the traditional (selfish) system. The results demonstrate a trade-off between cost-effective, unsustainable, selfish MG and marginally more costly, sustainable, collaborative MG. The 24-step COU model achieves an ideal compromise, reducing the SOU by more than half while exhibiting a slightly higher levelized cost of energy than the selfish MG. The fourth article investigated the volatility absorption function of the collaborative MG while facing real-world uncertainties, such as forecasting errors, demand fluctuations and extreme weather events. The findings indicate that the volatility absorption effect of collaborative MG has shown a high resilience towards seasonal effects, particularly the 24-step scenario, which demonstrates a high adaptability to all seasonal fluctuations. It also demonstrates a high resilience in response to varying demand profiles. In the context of extreme weather events, specially in the absence of wind, the collaborative MG diminished the SOU by 93% in contrast to the selfish MG.
This thesis primarily contributes to the literature by proposing a method to regulate the volatility of renewable energy outputs through collaborative MGs, which can significantly increase the penetration of renewables in power networks by implementing the COU operational strategy. This research studied the collaboration between the MG and the utility grid at the interconnected houses-scale MG which include an energy storage and local generation from solar and wind installations. However, the proposed collaborative strategy could be applied at different scales. The outcomes of this research show the efficiency of the proposed collaborative strategy and show the potential for future research that could scale up the application of collaborative MGs to evaluate its aggregate impact on energy systems.

Date of Award	6 Mar 2026
Original language	English
Awarding Institution	University of Portsmouth
Supervisor	Michel Leseure (Supervisor), Jovana Radulovic (Supervisor) & Hom Dhakal (Supervisor)