Álvaro Manso-Burgos

The integration of battery energy storage systems within renewable energy communities offers a promising avenue to provide grid flexibility through balancing markets. While distributed storage is often financially unviable solely for self-consumption in small-scale residential loads, this study demonstrates that market participation significantly increases community revenues, transforming communal batteries into highly profitable investments. However, participating in bidirectional automatic frequency restoration reserve markets significantly alters local physical energy flows. We reveal that traditional revenue-sharing schemes fail to capture these economic distortions, unintentionally generating a free-rider effect where certain prosumers accrue unearned energy savings while others incur opportunity costs. To address this structural challenge, we develop a mixed-integer linear programming model to simulate an energy community’s participation in automatic frequency restoration reserve markets and conduct a comparative analysis of four benefit allocation mechanisms. We propose a novel ex-post approach, the Net Opportunity Cost Allocation, which utilises a shadow settlement algorithm based on deterministic smart meter data to virtually reconstruct the pure self-consumption baseline. By structurally aligning the physical grid’s impacts with financial market revenues, this method explicitly adjusts each member’s market share to offset any unintended economic gains or losses. The results confirm that this mechanism completely neutralises the free-rider effect, ensuring objective financial neutrality for all participants. Ultimately, this transparent and easily implementable framework increases revenue and profitability for the energy community while ensuring that no user is penalised by market activations, thereby fostering the long-term social trust required to accelerate the widespread deployment of distributed energy resources and storage in energy communities.

The integration of battery energy storage systems within renewable energy communities offers a promising avenue to provide grid flexibility through balancing markets. While distributed storage is often financially unviable solely for self-consumption in small-scale residential loads, this study demonstrates that market participation significantly increases community revenues, transforming communal batteries into highly profitable investments. However, participating in bidirectional automatic frequency restoration reserve markets significantly alters local physical energy flows. We reveal that traditional revenue-sharing schemes fail to capture these economic distortions, unintentionally generating a free-rider effect where certain prosumers accrue unearned energy savings while others incur opportunity costs. To address this structural challenge, we develop a mixed-integer linear programming model to simulate an energy community's participation in automatic frequency restoration reserve markets and conduct a comparative analysis of four benefit allocation mechanisms. We propose a novel ex-post approach, the Net Opportunity Cost Allocation, which utilises a shadow settlement algorithm based on deterministic smart meter data to virtually reconstruct the pure self-consumption baseline. By structurally aligning the physical grid's impacts with financial market revenues, this method explicitly adjusts each member's market share to offset any unintended economic gains or losses. The results confirm that this mechanism completely neutralises the free-rider effect, ensuring objective financial neutrality for all participants. Ultimately, this transparent and easily implementable framework increases revenue and profitability for the energy community while ensuring that no user is penalised by market activations, thereby fostering the long-term social trust required to accelerate the widespread deployment of distributed energy resources and storage in energy communities.

Energy communities are increasingly recognised as a promising model for supporting the transition towards decentralised and decarbonised energy systems. By enabling local renewable generation, collective self-consumption and active participation of end users, they offer new opportunities for improving system efficiency and resilience. In this context, demand flexibility can play a key role by allowing electricity consumption to adapt to the variability of renewable generation. However, in most existing studies, demand flexibility is primarily treated as a complementary resource, while its broader potential and its implications for the planning and operation of ECs remain largely unexplored. In particular, it remains unclear how demand flexibility influences the economic viability of ECs and to what extent it can substitute for or complement storage deployment. This paper therefore investigates how demand flexibility influences the optimal design and operation of ECs. An optimisation model of community energy management is developed and applied to an agricultural case study in the Spanish region of Valencia, where irrigation demand represents a flexible and controllable load. The results show that leveraging demand-side flexibility leads to a 45% reduction in annual energy costs compared to a non-flexible configuration. They also indicate that when demand is already highly flexible the integration of battery storage does not necessarily generate additional savings. Moreover, the correct choice of tariff scheme types and energy allocation coefficients strongly influences the system performance. These findings highlight the techno-economic value of demand flexibility and provide insights for the design and operation of cost-effective ECs.

Renewable energy sources such as PV solar or wind power are intermittent and non-dispatchable. Massive integration of these resources into the electric mix poses some challenges to meeting power generation with demand. Hence, improving power generation forecasting has raised much interest. This work assesses the market value of enhanced PV solar power generation forecasting. Then, we analyse the different agents present in the electricity system. We link the studied agents to the proposed market values based on both analyses. Improving the accuracy of RES forecasting has massive potential as the sector grows and new agents arise. It can have reactive values like reducing imbalances or proactive values such as participating in intraday markets or exercising energy arbitrage. However, accurate forecasting can also lead to opportunistic values that can be exploited by malicious agents if they are not adequately regulated.

Massive integration of these resources into the electric mix poses some challenges to meet power generation with demand. Hence, improving power generation forecasting has raised much interest. This work assesses the market value of PV forecasting and the the different agents present in the electricity system. Based on both analyses, we link the studied agents to the proposed market values. Improving RES forecasting has potential as the sector grows and new agents arise. We map these values depending on their nature as proactive, reactive and opportunistic, then we correlate them with the different types of agent.

The European Union advocates for legislative support to local energy communities. Measures include the promotion of dynamic energy allocation and discriminatory electricity tariffs such as the recent Spanish framework. However, the impact of these normative changes is not yet evaluated. This paper inquires into the impact of dynamic allocation coefficient and different electricity tariffs on the profitability of local energy communities. To do so, a linear optimisation model is developed and applied to real consumer data in Spain around a variable capacity photovoltaic generation plant. Comparing the economic performance of the static or variable power allocation under the effect of changing electricity tariffs. While both measures are beneficial, the new electricity tariffs result in larger profitability increases than the planned variable coefficients. The combination of measures allows for profitability improvements of up to 25% being complementary measures. However, installations that maximise the potential for electricity generation are still not as profitable due to the low purchase price of surplus energy. While discriminatory electricity price tariffs and variable allocation coefficients are positive measures, further measures are needed for these communities to install generation plants as large as the potential that each case allows.