Abstract
Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building's Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings' energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors' laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems.

Abstract
The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6-11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

Abstract
This paper presents MARTINE (Multi-Agent based Real-Time INfrastruture for Energy), a simulation, emulation and energy management platform for the study of problems related to buildings and smart grids. Relevant advances related to buildings and smart grid management and operation have been proposed, focusing either on software models for decision support or on physical infrastructure and control approaches. These two perspectives are, however, complementary, and no practical assessment can be achieved without a suitable interaction and analysis of the impact that decision-making models have on physical resources, and vice-versa. MARTINE overcomes this limitation by integrating, in a single platform: real buildings with the associated devices and resources; emulated components that complement the ones present in the buildings; simulated resources, players and buildings using multi-agent systems, real-time simulation with hardware in the loop capabilities, which enables integrating virtual and physical components; and a knowledge layer that incorporates all the required decision support and energy management models. MARTINE thus provides a comprehensive platform for the study and management of energy resources. The advantages of this platform are demonstrated in this paper through three use cases, related to agriculture irrigation, practical implementation of demand response and load modeling using various network configurations.

Abstract
The worldwide investment in renewable energy sources is leading to the formation of local energy communities in which users can trade electric energy locally. Regulations and the required enablers for effective transactions in this new context are currently being designed. Hence, the development of software tools to support local transactions is still at an early stage and faces the challenge of constant updates to the data models and business rules. The present paper proposes a novel approach for the development of software tools to solve auction-based local electricity markets, considering the special needs of local energy communities. The proposed approach considers constrained bids that can increase the effectiveness of distributed generation use. The proposed method takes advantage of semantic web technologies, in order to provide models with the required dynamism to overcome the issues related to the constant changes in data and business models. Using such techniques allows the system to be agnostic to the data model and business rules. The proposed solution includes the proposed constraints, application ontology, and semantic rule templates. The paper includes a case study based on real data that illustrates the advantages of using the proposed solution in a community with 27 consumers.

Abstract
This paper presents a study on the impact of adjacent markets on the electricity market, realizing the advantages of acting in several different markets. The increased use of renewable primary sources to generate electricity and new usages of electricity such as electric mobility are contributing to a better and more rational way of living. The investment in renewable technologies for the distributed generation has been creating new opportunities for owners of such technologies. Besides the selling of electricity and related services (ancillary services) in energy markets, players can participate and negotiate in other markets, such as the carbon/CO2 market, the guarantees of origin market, or provide district heating services selling of steam and hot water among others. These market mechanisms are related to the energy market, originating a wide market strategy improving the benefits of using distributed generators. This paper describes several adjacent markets and how do they complement the electricity market. The paper also shows how the simulation of electricity and adjacent markets can be performed, using an electricity market simulator, and demonstrates, based on market simulations using real data from the Iberian market, that the participation in various complementary markets can enable power producers to obtain extra profits that are essential to cover the production costs and facilities maintenance. The findings of this paper enhance the advantages for investment on energy production based renewable sources and more efficient technologies of energy conversion.

Abstract
The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles' flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle's users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users' trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.