An energy community is a legal figure, recently coined by the European Union, that creates a framework to encourage active participation of citizens and local entities in the energy transition to net-zero. In this work, we study the optimal participation of energy communities in day-ahead, reserve, and intraday electricity markets.

The motivation to do so is that there are time periods where energy communities cannot meet their internal demand, and periods where they generate excess electricity. This is because the electricity they generate often comes from variable renewable resources like solar and wind. Electricity market participation is a natural way to ensure they meet their internal demand at all times, and, simultaneously, make the most of the excess electricity.

We propose a multi-stage stochastic programming model that captures variable renewable and electricity price uncertainty. The multi-stage aspect models the different times at which variable renewable generation is considered to be known and electricity prices from different markets are revealed. This results in a very large scenario tree with 34 stages, and hence a very large optimization problem. Scenario reduction techniques are applied to make the problem tractable. Case studies with real data are discussed, considering different energy community configurations, to analyse proposed regulatory frameworks in Europe. The added value of considering stochasticity in this problem is also analysed.

The rise in energy transition incentives has increased the prevalence of distributed energy resources (DERs), posing both challenges and opportunities for the electric grid. We propose a cooperative approach to optimize individual load consumption providing on-demand flexibility to support the distribution system operator (DSO). We introduce a hierarchical optimization strategy using two distinct timescales. First, a day-ahead phase coordinates loads and sets hourly individual setpoints. The objective is to adjust the aggregated consumption to follow a regulation consumption level provided by the DSO. The regulation signal represents the optimal aggregated consumption, enabling the DSO to ensure efficient network operations. At the day-ahead level, we consider uncertainties inherent to the problems through chance constraints. This allows us to account for intrinsic unpredictability of DERs, customer baseloads, and DSO targets. Additionally, we integrate power flow constraints into the day-ahead level to prevent exceeding physical network limits. Individual setpoints evaluated for each user are then utilized in the second optimization step where real-time implementation is considered. Each load employs model predictive control to track their corresponding individual setpoint while subject to only local information and uncertainties. Lastly, we propose a cooperative game-theoretic mechanism based on marginal contributions to provide a scalable and fair allocation of the aggregation payoff over all participating users. The approach is numerically implemented to demonstrate its ability to provide flexibility to modern power systems.

Social infrastructure systems such as police, hospitals and banking services, rely heavily on civil infrastructure systems, like power and telecommunications, to provide their services. However, the interdependencies between social and civil infrastructure systems are rarely modeled, as they are in this modeling effort. This work extends an interdependent network optimization model through the development of a capacity expansion model for electricity generation, to optimally provide energy to both civil and social infrastructure post-disaster. The model is based on a detailed representation of a community that faces repeated natural disasters, including hurricanes and flooding – the CLARC community. The results analyze the impacts of both infrastructure outage level and outage type on energy system design. Energy capacity location and technology type is highly dependent on the availability of the energy transmission network, which limits the effectiveness of large-scale technologies like nuclear power.