Abstract
In the context of the R3EA project, funded by the Portuguese Foundation for Science and Technology (FCT), we analyse a set of selected future power system scenarios to assess the impact, on the Iberian electricity market (MIBEL), of installing wind and solar generation capacity in Portugal's Centro Region. We use the long-term MIBEL operation and planning model CEVESA. The scenarios are designed based on the current economic situation and the last National Energy and Climate Plan drafts for Portugal and Spain, by distributing the expected new wind and solar generation capacity differently among Portugal regions, also considering the flexible demand for producing electrolytic hydrogen. Market prices, capture prices and production per technology are analysed to assess this impact. Results show that regional investments have no significant impact on the MIBEL variables analysed.

Abstract
Self-consumption regulations are leading to the emergence of new business models proposed by new players and causing traditional players to make new proposals to take advantage of the new business opportunities. In this context, traditional retailers are assessing self-consumption business models, offering management services for self-consumption structures, or the installation of distributed resources, such as solar panels or batteries. Some of the new business models being proposed by electricity suppliers are related to virtual battery services. Indeed, suppliers can, in the free retail market, create innovative tariffs, and design them to make their customers believe they own and manage a battery, even if it does not correspond to a physical battery in the grid. This paper analyses the business model of a supplier offering a virtual battery service, comparing it to the installation of a physical battery, showing that it has no significant benefits compared to more simple approaches.

Abstract
The integration of renewable generation requires new sources of flexibility, including the flexibility from distributed resources that can be unlocked via local flexibility markets (LFMs). In these markets, aggregators (AGGs) offer the flexibility from their portfolios to the flexibility requesting parties (FRP), i.e. system operators or other balancing requesting parties. To bid in LFMs and manage market uncertainty, AGGs must compute the flexibility they are willing to offer at each possible flexibility market price, by optimizing their portfolios. This paper proposes a 2-stage methodology to compute the flexibility bidding curve that an energy community can send to a LFM when behaving as an AGG of its members resources. At stage 1, the energy community (EC) manager computes the optimal EC operation without flexibility provision, minimizing the EC energy bill, and serving as the baseline to verify the flexibility provision. Then, at stage 2, for each possible flexibility price, the EC manager computes the optimal flexibility to be offered, minimizing the EC energy bill but including the flexibility provision incomes, to build the flexibility bidding curve.

Abstract

Abstract

Abstract
As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as shoeboxes using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.