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Current developments in the renewable energy field, and the trend toward self-production and self-consumption of energy, has led to increased interest in the means of storing electrical energy; a key element of sustainable development. This book provides an in-depth view of the environmentally responsible energy solutions currently available for use in the building sector. It highlights the importance of storing electrical energy, demonstrates the many services that the storage of electrical energy can bring, and discusses the important socio-economic factors related to the emergence of smart buildings and smart grids. Finally, it presents the methodological tools needed to build a system of storage-based energy management, illustrated by concrete, pedagogic examples.

INTRODUCTION: This paper presents a distributed approach to optimise self-consumption on a local energy community containing photovoltaic generators, electric vehicles, loads and a storage system. OBJECTIVES: The goal is to maximise energy sharing between users while preserving the indivual objectives of each user. METHODS: Game theory is employed to model users’ behavior and preferences. A distributed algorithm is used to solve the optimisation problem. In addition, a physical model of the grid is built to verify if the solutions respect grid constraints. Finally, a private blockchain environnement is deployed to concretely implement this distributed framework with a smart contract. RESULTS: It is shown that the proposed approach effectively leads to an increase of self-consumption rate on the local grid. CONCLUSION: The proposed distributed framework, combining game theory and blockchain, shows real potential to improve energy sharing on energy communities.

This book deals with the management and valuation of energy storage in electric power grids, highlighting the interest of storage systems in grid applications and developing management methodologies based on artificial intelligence tools.

The opening of the railway use all around Europe carried plenty of technical challenges never seen before in the railway field. An important challenge in energy billing filed, is to precisely do the billing of the consumed energy for each railway company in a reliable way. For that, in ground applications, RTE (the French national transmission system operator, who is in charge of the energy metering in railways by law in France) normally installs two energy meters to ensure the reliability of the measurements. Nevertheless, in agreement with the European law, the installation of only one energy meter is mandatory in the new/refurbished rolling stock since 2014. To ensure the correct billing of the railway companies, the data collected by the energy meters must be validated. In other cases no measure is available due to different technical problems, therefore a system that estimates the energy consumptions of those journeys is necessary. In this paper, a validated dataset is used to reconstruct the amount of energy consumed by trains whose energy meter data is missing or not valid. The validated data will be the start point for different estimators of the consumed energy by the trains. This dataset is composed of the information collected from the energy meters (date information and GPS position of the train) and train run data (transit times on fixed points of the line). Different types of estimators for the energy reconstruction are compared with real measurement data from a regional train circulation for a couple of years. Finally, a discussion of the performance of the different estimators will be shown.

To maintain a power reserve to contribute to the primary frequency control with variable speed wind generators, two control strategies are analysed in this paper: a torque control strategy and a fuzzy logic based multivariable control strategy. The first and most simple strategy allows to maintain a regular reserve only when the wind turbine generator works at full load and needs the determination of a power reference value a priori or by the network manager. The second control strategy allows to overcome these limits without any wind speed measurement; it determines in real time the generator power reference value to maintain a fixed minimum reserve. The main result of this paper is to shown that it is possible to maintain a regular reserve on a large range of the wind speed. This reserve is still more regular when considering a wind farm owing to the wind speed dispersion. The performance of the proposed primary frequency control strategies is shown with the help of simulations by considering a wind farm including three wind generators.

One of the main elements of the evolution of smart grids is the shift from consumers to “prosumers”. This evolution concerns consumers with their own local renewable production sources and/or storage systems of any size, or who, more generally, possess storage capacity. The motivations involved may vary according to socio‐economic profiles, including economic, ecological and technological aspects; some actors remain indifferent, whereas others are strongly opposed to these developments. The chapter considers the different rationalities of actors in smart grids, which may vary widely and affect whole groups. It discusses the economic and social implications of smart grids, including changes to the chain of value, contract models, the socio‐economic profiles of consumers or prosumers and regulation. The chapter also considers the social acceptability of participation in energy management, specifically in the context of energy balancing in a multi‐actor commercial building (shopping mall) and in a domestic setting (households in residential buildings).

This chapter describes an energy management strategy based on fuzzy logic, applied to a DC network architecture. It shows the proposed architecture connecting various power devices. A DC bus, with a minimum variable voltage of 200V, is used to connect photovoltaic panels, LED lighting systems running on 48VDC and a battery storage system running on 48VDC, while maintaining a connection to the AC electrical network. Real‐time energy management of the system is based on the first six steps of the methodology, using fuzzy logic. These steps are: determination of a system specification, with objectives, constraints and resources clearly laid out; determination of a supervisor structure; and determination of a functional graph. The steps also include: determination of membership functions for input and output variables, enabling the translation of system variables into fuzzy variables; determination of the operating graph; and creation of fuzzy rules based on these operating graphs.

This chapter develops an energy management system for a commercial building, such as a supermarket, including photovoltaic (PV) solar production and energy storage. It describes a large supermarket connected to the electric grid, with a solar power installation on the roof and a storage system. An energy management strategy for the storage system are developed using fuzzy logic. The chapter begins by defining the connection configuration of the supermarket. The objectives, constraints and means of action in this supervision are also presented, along with the principles of electric billing and of the energy management strategy. Next, the chapter develops a supervisor based on fuzzy logic. It presents the results of simulations, and different topologies (with or without PV power) are compared using economic and ecological indicators. The PV system and the supermarket load are modeled by the production profile and load profile, respectively.

This chapter presents a photovoltaic system with hybrid storage combining two different technologies: batteries and supercapacitors. This hybrid approach combines the advantages of each technology in order to increase the life expectancy of batteries and to improve yield from the whole system. The system discussed in the chapter is intended to supply electricity to residential habitat in insular or isolated environments. The chapter develops a smart supervision algorithm based on fuzzy logic. It also presents a comparative study of different storage configurations, paying particular attention to the life expectancy of storage elements and to the levelized cost of energy. The chapter provides a detailed overview of photovoltaic systems for use in buildings, with or without a connection to the grid. It also describes the importance of storage in these systems, and gives a detailed description of the steps and indicators used in evaluating energy management.

The electrical energy consumed by buildings may be produced locally or supplied by the distribution grid. Buildings are generally powered by the grid, unless they are isolated (e.g. a mountain chalet) or have their own power supply. This situation is increasingly common with the development of renewable wind and photovoltaic solar supplies. This chapter shows typical profiles for domestic and commercial consumers. It also shows the way in which consumption varies depending on the time of day, the season and the load type. Buildings have a key role to play in the development of smart energy grids, micro‐grids, eco‐neighborhoods and smart cities. The chapter shows different characteristics of a smart building, including local energy production and storage and controllable loads with modulable consumption (lighting, heating, electric vehicles, etc.), which may be connected to the electricity distribution grid and to external sources or in islanded mode, i.e. cut off from the main grid.