Abstract
Local Electricity Markets introduces the fundamental characteristics, needs, and constraints shaping the design and implementation of local electricity markets. It addresses current proposed local market models and lessons from their limited practical implementation. The work discusses relevant decision and informatics tools considered important in the implementation of local electricity markets. It also includes a review on management and trading platforms, including commercially available tools. Aspects of local electricity market infrastructure are identified and discussed, including physical and software infrastructure. It discusses the current regulatory frameworks available for local electricity market development internationally. The work concludes with a discussion of barriers and opportunities for local electricity markets in the future. © 2021 Elsevier Inc.

Abstract
Smart grid (SG) technologies are playing a key role in the electric grid transformation, bringing out promising benefits for different actors and empowering customers. However, this transition imposes new challenges concerning the operation and management of energy, particularly at the distribution level of the electric grid. This chapter provides an overview of achieved advances toward the widespread implementation of SG, including technological and infrastructure developments. Transactive energy presents a distributed decision-making coordination approach using automated energy transactions that is enabled by the intelligence and connectivity benefits of SG. The way in which these transactions can be integrated in a local market environment and how advances in transactive energy, supported by the infrastructure already developed to enable SG, are leading to the emergence of local energy markets is discussed in this chapter. © 2021 Elsevier Inc.

Abstract
The local flexibility market models have emerged as a market-based solution to respond to the challenges that the increase in distributed energy resources caused in the power and energy systems. Using Smart Grid enabling technologies, consumers and prosumers are prepared to respond to any possible demand-side flexibility event. In this scope, this work presents an advanced bidding model for the prosumers/consumers' participation in a local flexibility market to solve existing issues in the local grid. The proposed advanced model consists of a single-sided auction-based clearing method where prosumer offers are ranked and chosen according to the price and other characteristics, such as their location and distance to the problem to be solved. The aim is to prioritize and select the offers that have a more positive impact on the situation to solve at the lowest possible cost. © 2022 IEEE.

Abstract
The growth of renewable energy sources usage at the local level contributes to decentralizing the power and energy systems. Nowadays, there is an increment of residential consumers becoming prosumers able to consume their generation or sell it to the public grid to reduce the electricity bill. This great penetration of electricity compromises the proper functioning of the system. Local electricity markets (LEM) are market platforms aimed at electricity end-users to be able to negotiate and transact it between them, thus becoming active players in the system, being a possible solution to balance local systems. Different approaches for LEM design and implementation are proposed in the literature, usually based on community markets and peer-to-peer. Despite their value, these solutions' scalability is compromised as these are centralized solutions, and processing can become very heavy. In this sense, this work proposes a blockchain-based distributed and decentralized optimal solution for implementing LEM. © 2022 IEEE.

Abstract
The integration of distributed energy resources contributes to accomplishing a balance between the supply and demand inside a local market. The operation of these markets is based on the peer-to-peer negotiations between users, whose cooperation leads to an increase in the social welfare of the community, thus creating a more user-centric market. This work fits in the context of the energy community, where members of a community can exchange energy in peer-to-peer transactions and use the public electricity grid as a backup. The market aims at maximizing the social welfare of the community considering the operational costs of all community members. A particle swarm optimization algorithm implemented in Python is used to solve the problem. © 2022 IEEE.

Abstract
Recent commitments and consequent advances towards an effective energy transition are resulting in promising solutions but also bringing out significant new challenges. Models for energy management at the building and microgrid level are benefiting from new findings in distinct areas such as the internet of things or machine learning. However, the interaction and complementarity between such physical and virtual environments need to be validated and enhanced through dedicated platforms. This chapter presents the Multi-Agent based Real-Time Infrastructure for Energy (MARTINE), which provides a platform that enables a combined assessment of multiple components, including physical components of buildings and microgrids, emulation capabilities, multi-agent and real-time simulation, and intelligent decision support models and services based on machine learning approaches. Besides enabling the study and management of energy resources considering both the physical and virtual layers, MARTINE also provides the means for a continuous improvement of the synergies between the Internet of Things and machine learning solutions.