Abstract
This work presents a risk analysis performed to stationary Li-ion batteries within the framework of the STABALID project. The risk analysis had as main objective analysing the variety of hazards and dangerous situations that might be experienced by the battery during its life cycle and providing useful information on how to prevent or manage those undesired events. The first task of the risk analysis was the identification of all the hazards (or risks) that may arise during the battery life cycle. Afterwards, the hazards identified were mapped in the different stages of the battery life cycle and two analyses were performed for each stage: an internal problem analysis and an external peril analysis. For both, the dangerous phenomena and the undesirable events resulting from each hazard was evaluated in terms of probability of occurrence and severity. Then, a risk assessment was carried out according to a predefined risk matrix and a preliminary set of risk mitigation measures were proposed to reduce their probability of occurrence and/or their severity level. The results obtained show that it is possible to reduce the probability of occurrence/severity of all the risks associated to the battery life cycle to acceptable or tolerable levels.

Abstract
Vast increase of renewable energy resources' (RER) share in total electricity production have led to evolving studies regarding different aspects of renewables integration. Other than their effects on network, the electricity markets are also affected by uncertain behavior of RERs in the market place. Hence, new approaches for market clearing are investigated. One of the possible solutions is the deployment of stochastic market clearing. However, the adaption of new market models should consider different market characteristics. As a result, this paper assesses the adaption of stochastic market in a hydro-penetrated system. The co-optimized energy and reserve schedule in the day-ahead time frame is derived using the mixed integer linear programming (MILP). The model is tested with Portuguese electricity market data as a real-case of hydro-penetrated system.

Abstract
This paper addresses the participation of an aggregator of small prosumersin the energy and tertiary reserve markets. A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers. The aim is to define energy and tertiary reserve bids to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real-time stage. Scenario-based stochastic programming is used to deal with the uncertainties of photovoltaic power generation, electricity demand, outdoor temperature, end-users' behavior, and preferences. A case study of 1000 small prosumers from MIBEL is used to compare the proposed strategy to two other strategies. The numerical results show that the proposed strategy reduces the bidding net cost of the aggregator by 48% when compared to an inflexible strategy typically used by retailers.

Abstract
This paper addresses the bidding problem faced by an aggregator of small prosumers, when participating in the day ahead energy market. A two-stage stochastic optimization model is proposed to support the aggregator in the definition of optimal and robust demand and supply bids. Stochastic programing is used to deal with uncertainty of end-users' behavior, outdoor temperature, electricity demand and PV generation. The proposed approach was compared to other benchmark strategy, using a case study of 1000 prosumers from the Iberian market.

Abstract
The uptake of electric vehicles (EV) will require important modifications in traditional grid planning and load forecasting techniques. Existing literature suggests that the integration of EVs will be more adversarial to elements of the existing electricity infrastructure in terms of power supply (kW) than energy (kWh) delivery. While several studies analyzed the grid impact of electric vehicle fleets, few consider the adoption process itself which may lead to strong spatial variations of the utilization of charging infrastructure. The presented approach extends spatial load forecasting, introducing diffusion theory elements to analyze spatio-temporal clustering of EV charging demand. Using open-access census and grid data, this work develops a deterministic framework to forecast spatial patterns of EV charging applied to a real-world environment. Outcomes suggest substantial spatial clustering of EV adoption patterns, showing substation overrating for EV penetration rates of 25% and above with 7.4kW charging power.

Abstract
This paper addresses the problem faced by an aggregator of small prosumers, when participating in the energy market. The aggregator exploits the flexibility of prosumers' appliances, in order to reduce its market net costs. Two optimization procedures are proposed. A two-stage stochastic optimization model to support the aggregator in the definition of demand and supply bids. The aim is to minimize the net cost of the aggregator buying and selling energy at day-ahead and real-time market stages. Scenario-based stochastic programing is used to deal with the uncertainty of electricity demand, end-users' behavior, outdoor temperature and renewable generation. The second optimization is a model predictive control method to set the operation of flexible loads in real-time. A case study of 1000 small prosumers from the Iberian market is used to compare four day-ahead bidding strategies and two real-time control strategies, as well as the performance of combined day-ahead and real-time strategies. The numerical results show that the proposed strategies allow the aggregator to reduce the net cost by 14% compared to a benchmark typically used by retailers (inflexible strategy).