Abstract
The current energy strategy of the European Union puts the end-user as a key participant in electricity markets. The creation of energy communities has been encouraged by the European Union to increase the penetration of renewable energy and reduce the overall cost of the energy chain. Energy communities are mostly composed of prosumers, which may be households with small-size energy production equipment such as rooftop photovoltaic panels. The local electricity market is an emerging concept that enables the active participation of end-user in the electricity markets and is especially interesting when energy communities are in place. This paper proposes an optimization model to schedule peer-to-peer transactions via local electricity market, grid transactions in retail market, and battery management considering the photovoltaic production of households. Prosumers have the possibility of transacting energy with the retailer or with other consumers in their community. The problem is modeled using mixed-integer linear programming, containing binary and continuous variables. Four scenarios are studied, and the impact of battery storage systems and peer-to-peer transactions is analyzed. The proposed model execution time according to the number of prosumers involved (3, 5, 10, 15, or 20) in the optimization is analyzed. The results suggest that using a battery storage system in the energy community can lead to energy savings of 11-13%. Besides, combining the use of peer-to-peer transactions and energy storage systems can potentially provide energy savings of up to 25% in the overall costs of the community members.

Abstract
This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sense, as, by combining the results and use of multiple learners, is able to find new ways for current learning models to be used and optimized. In this paper three ensemble learning models are developed and the respective results compared: gradient boosted regression trees, random forests and an adaptation of Adaboost. Results for electricity consumption forecasting in hour-ahead are presented using a case-study based on real data from an office building. Results show that the adapted Adaboost model outperforms the reference models for hour-ahead load forecasting.

Abstract
Recent changes in the energy sector are increasing the importance of portfolio optimization for market participation. Although the portfolio optimization problem is most popular in finance and economics, it is only recently being subject of study and application in electricity markets. Risk modeling in this domain is, however, being addressed as in the classic portfolio optimization problem, where investment diversity is the adopted measure to mitigate risk. The increasing unpredictability of market prices as reflection of the renewable generation variability brings a new dimension to risk formulation, as market participation risk should consider the prices variation in each market. This paper thereby proposes a new portfolio optimization model, considering a new approach for risk management. The problem of electricity allocation between different markets is formulated as a classic portfolio optimization problem considering market prices forecast error as part of the risk asset. Dealing with a multi-objective problem leads to a heavy computational burden, and for this reason a particle swarm optimization-based method is applied. A case study based on real data from the Iberian electricity market demonstrates the advantages of the proposed approach to increase market players? profits while minimizing the market participation risk.

Abstract
Electricity markets are complex and dynamic environments with very particular characteristics. Ambitious goals, including those set by the European Union, foster the increased use of distributed generation, essentially based on renewable energy sources. This requires major changes in electricity markets and energy systems, namely through the adoption of the smart grid paradigm. The use of simulation tools and the study of different market mechanisms and the relationships between their stakeholders are essential. One of the main challenges in this area is developing decision support tools to address the problem as a whole. This work contributes to increasing interoperability between heterogeneous systems, namely agent-based, directed to the study of electricity markets, the operation of smart grid, and energy management. To this end, this work proposes the use of ontologies to ease the interaction between entities of different natures and the use of semantic web technologies to develop more intelligent and flexible tools. A multiagent systems society, composed of several heterogeneous multiagent systems, which interact using the proposed ontologies, is presented as a proof-of-concept. © 2021, Springer Nature Switzerland AG.

Abstract
Peer-to-Peer (P2P) energy trading (ET) is a paradigm of energy trading between consumers without intermediaries. This model of ET allows the commercialization of energy resources produced through renewable sources that do not need other local consumers. This energy trading between consumers is able to improve the local balance of energy generation and consumption. Currently, this paradigm is being evaluated to show the suitability of its application in today’s society, significantly increasing the number of projects in this area worldwide. This article reviews the main models of application of this paradigm in smart cities, presenting the main characteristics of these approaches. This paper proposes an architectural model for P2P energy trading that solves the main deficiencies detected. The designed system allows the simulation of P2P processes using a novel negotiation model. This energy trading system is based on a Multi-Agent System (MAS) using the Robot Operating System (ROS). The system allows representing using independent agents each one of the zones that intervene in the process of negotiation of the energy of a city, being already representing a consumer’s role or a producer’s role of energy. The system has been tested on a model in which each zone uses real data about the role it simulates over a period of two and a half years. The preliminary results show how the energy trading market allows a smart city to evolve towards a high degree of sustainability. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract