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The increasing electrical power generation from renewable energy resources poses new challenges to the electrical grid. These challenges are mainly related to the inconsistent nature of renewable energies and the burden on transmission lines due to the increasing generated power. In this paper, the correlation between renewable energy generation and the transmission capability of electrical power lines is studied. In order to facilitate the accommodation of renewable energies the actual dynamic line rating (DLR) is estimated based on actual weather conditions. To achieve this purpose a new DLR estimation method using machine learning is presented. Line rating depends on various meteorological factors (wind speed, wind direction, ambient temperature, and solar radiation). A machine-learning model was trained using Random Forest (RF) regressor to estimate the rating of a transmission line based on these meteorological factors. The data used for model training is actual measurements of weather conditions, conductor temperature, and conductor current. Results, where the trained model is used to estimate the rating of an ACSR Lynx conductor with a thermal limit of 80°C, are presented.

With increasing energy prices, nuclear energy concerns, climate changes, and electrical grid losses, the demand to rely on more renewable energy is growing. The majority of people currently prefer to live and work in smart environments, such as smart cities and smart institutions with an integrated smart microgrid. Energy management is a complex topic because a large amount of these smart microgrid systems rely on hybrid energy sources. As a result, a smart energy management controller needs to be created. The current research provides a new energy management control technique for a smart DC-microgrid based on a combined fuzzy logic controller (FLC) and high order sliding mode (HSMC) methods. The hybrid energy provider integrated into the DC-microgrid is made up of a battery bank, wind energy, photovoltaic (PV) energy, and tidal energy source. The new proposed intelligent control is intended to regulate source-side converters (SSCs) in order to capture the maximum energy from hybrid renewable energy sources (wind, tidal and PV) while also improving power quality in the DC-microgrid. To keep the microgrid as cost-effective as feasible, renewable energy sources are prioritized. The suggested controller offers a steady output power and sustained service. From the present simulation results under Matlab/Simulink and the comparative analysis, the proposed controller produces +1.02% wind power, +10% PV power, +100% tidal power, and +8.48% load power over intelligent fractional-order proportional–integral–derivative (PID) and more when compared to the super twisting fractional-order and PID controls. In addition, the suggested controller assures smooth output power and uninterrupted service.

Electricity demand is expected to grow in the upcoming years due to the electrification of transport, which will likely result in an increase in electricity peak demand when charging at home; this would not represent a problem for the electric vehicle (EV) owner but could potentially destabilise the grid. This work has compared the use of stationery and vehicle-to-home (V2H) energy storage systems to minimise the electricity bill for the household consumers. The impact of using different electricity tariffs and the peak demand derived by this was also investigated. Real-world data was used to model the availability of the EVs to provide V2H during the day. Constraints to guarantee adequate charging of the EV to ensure the ability to provide transportation have been implemented. Two different stationary batteries and two EVs were used for the simulations. High peaks on the demand of up to 6 kW per vehicle (the bi-directional charger’s maximum capacity) and up to 5 kW (the batteries charger’s maximum capacity) are expected every time that the electricity price drops, and low peaks are expected when the electricity price increases. Moreover, high peaks are expected mostly at night when the electricity price tends to be cheaper and/or after driving the EV and plug it again to charge, however the model will try to constraint the charging of the EV until the price is low again unless there is a journey likely to happen in the near future. The combination of PV generation with a stationary battery or a V2H technology can produce savings of at least 30% regardless the electricity tariff and a reduction of up to 85% in the electricity bill can be achieved under the Time-of-day tariff. The results give a perspective of what can the grid expect when charging an EV at home during winter.

This research is concerned with the novel application and investigation of `Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy flexibility. The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock. SAC is a model-free DRL technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems. Such control techniques are seen as one possible solution to supporting the operation of a smart, sustainable and future electrical grid. This research tests the suitability of the SAC DRL technique through training and deployment of the agent on an EnergyPlus based environment of the office building. The SAC DRL was found to learn an optimal control policy that was able to minimise energy costs by 9.7% compared to the default rule-based control (RBC) scheme and was able to improve or maintain thermal comfort limits over a test period of one week. The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables. The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates. It was found that the SAC DRL requires minimal training sample points and outperforms the RBC after three months of operation and also without disruption to thermal comfort during this period. The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended., Comment: submitted to Energy and AI

In this chapter, we show how it is possible to predict the day-ahead photovoltaic (PV) generation using machine learning (ML) techniques both at the level of an individual photovoltaic system and at the national level for the Italian case study. We address some critical issues related to the implementation of ML forecasting models/methods. We show two possible applications of the day-ahead PV generation forecast for PV producers/traders and for the Italian Transmission System Operator (TSO). The first deals with the day-ahead scheduling of the PV generation of utility-scale PV farms that PV producers/traders should deliver to the Italian TSO. The second is related to the imbalance between supply and demand at national level, hence to the prediction of the Italian net load made by the Italian TSO to plan the reserves needed to resolve the day-ahead production uncertainty. For both cases, we provide the economic value of solar forecasting under the current imbalance regulatory framework.

Renewable energy-based electricity production made significant progress in the recent past, and in the future it is expected to have a considerable contribution in power generation demography. A persistent need of renewable energy and the slow retirement of less efficient thermal power plant is inevitable. However, the variation in renewable production poses a challenge to a smooth and stable operation of a power grid. Hence, the operability of a nonrenewable energy power plant, especially a thermal power plant, plays a crucial role in the successful integration of renewable and stable grid operation. Systems and control aspects like modeling, estimation, prediction, and optimization play an important role in successfully tackling and improving the reliability and availability of a future electrical ecosystem. This chapter gives an overview of the challenges and opportunities for control systems from the perspective of steam-based power plants, particularly boiler operability in lieu of renewable energy integration. Also, a case study of exploring robust MIMO (Multi-Input-Multi-Output) control during a part load of a boiler with bypass control is presented. This scenario is expected to be common during renewable integration.

The power balance between the supply and demand side of an electrical grid is one of the most important issues in the grid operation. The forecasting of power generations and power demands is the essential premise for generation arrangement and power management. Historically, the conventional power grids were designed with large centralized generation plants where the generation followed the electricity demand; thus the balance was achievable to a certain level. However, the integration of renewable energy generators, as part of the smart grids, has complicated the process due to their uncertain and intermittent nature. The power imbalance caused by growing demands and peak loads leads to an increase in the pressure on the electrical infrastructure and perpetually escalating the energy prices and is significantly affecting the power reliability and quality. The peak load is usually resulted by the power demand characteristics of the end users. Although the duration of the peak load may be relatively short, the redundant capacities of power plants are still required to be standby to ensure the power demand can be met at any time. To maintain a cost-effective balance of power supply and demand, utilities have developed two mechanisms. The first mechanism is based on the integration of energy storage systems and the second mechanism is focusing on minimizing the peak load by encouraging the end users to change their power usage behaviors with incentive benefits. The first mechanism is mainly limited by its high initial and operation costs. The second mechanism is very dependent on the capabilities and the voluntaries of the end users. Utilities around the world have been considering demand-side management (DSM) in their strategic planning. Nevertheless, DSM programs should be carefully designed to incent the abilities of the end users at power demand side.

The exponential growth of intermittent renewable energy sources, such as wind and solar, and the global energy efficiency decarbonization campaign, are mainly driving increased interest in the storage of electrical energy. Current global electrical grid networks, however, are not capable of managing mass convergence of intermittent energy sources without significant network interruptions. The grid network is also accepted that more than 20% of intermittent renewable penitence can be significantly destabilized. Naturally, large-scale electricity storage technology can reduce the many intrinsic failures and weaknesses of the grid system, help improve grid efficacy, fully integrate intermittent renewable resources, and efficiently manage energy production. Electric energy storage provides two more critical advantages. First, it decouples electricity generation from the load- or energy user and simplifies the management of supply and demands. Second, it allows distributed storage opportunities for local grids or microgrids which greatly improve grid security and thus energy safety. This chapter aims to provide an extensive overview of a wide portfolio of techniques, equipment, and systems for the storage of electrical energy, and to present the latest advancement and difficulties that have yet to be overcome. Moreover, the chapter describes the status of storage techniques for mechanical, thermal, electrochemical, and chemical energy. It also offers background data on basic values for the interested nonexpert, where applicable, at the tutorial level. This chapter is expected to be of interest to both uninitiated and active researchers and engineers involved in energy storage techniques, with specific emphasis on large-scale storage of electrical energy.

Electric vehicle (EV) has received an intensive attention and been deployed globally due to its beneficial characteristics including higher energy efficiency and lower environmental impacts. However, massive charging of EV leads to several problems to electrical grid because of huge amount of electricity demand and its fluctuation. Therefore, charging management for EVs is urgently demanded. In this study, a novel battery-supported quick-charging system is developed and its performance in single and simultaneous multiple EVs charging is evaluated. The objective of battery installment includes maintaining the charging rate and reducing the burden of electrical grid due to EV charging. In addition, charging behavior of EV under different seasons (winter and summer) is clarified initially. As the results of the study, environmental ambient temperature influences strongly the behavior of EV charging. Charging during summer leads to higher charging rate, hence, shorter charging time. Furthermore, the developed charging system is able to facilitate simultaneous quick-charging for EVs in both winter and summer compared to conventional quick charging system.

This paper presents an innovative hybrid energy system for stable power and heat supply in offshore oil & gas installations (HES-OFF). The hybrid concept integrates offshore wind power with gas turbines and a H2 energy storage solution based on proton exchange membrane fuel cells and electrolysers. The objectives are: 1) improve the environmental performance of offshore installations by maximizing the exploitation of offshore wind and partially decarbonizing the gas turbines by co-feeding H2; 2) minimize the negative effects that wind power variability has on the electrical grid frequency stability. This study presents a first assessment of the HES-OFF concept performance using an offshore platform in the North Sea as case study. The results show that the HES-OFF concept: 1) cuts CO2 emissions up to 40 % when compared to the reference case but requires large H2 storage capacity to fully exploit wind power throughout the year; 2) allows higher wind power penetration without infringing on the grid frequency requirements.

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.

Hydropower is nowadays essential for balancing the electrical grid providing flexibility and fast response. The role of hydraulic turbines has changed from working at their Best Efficiency Point (BEP) to work in the whole operating range when demanded. Francis turbines working at off-design conditions suffer from dynamic problems that affect the useful life of their components, especially of the runner. Reduced scale physical models of Francis turbines are widely used to determine their hydraulic behavior. Most of the hydraulic parameters obtained in model tests are transposable to prototype by using the similarity theories as long as they meet similitude requirements. However, from the mechanical behavior point of view, both model and prototype structures are not always similar and the mechanical behavior of the model is often not considered for transposition. In this paper, a transposition method for the mechanical behavior of Francis turbines models is presented and the results are compared with the real size prototype. The modal behavior of the runner, its stress and fatigue life estimation for different operating conditions are experimentally compared for both model and prototype. Results present the possibilities and limitations of transposing the mechanical parameters from model to prototype.

Temporal variability in renewable energy presents a major challenge for electrical grid systems. Tides are considered predictable due to their regular periodicity; however, the persistence and quality of tidal-stream generated electricity is unknown. This paper is the first study that attempts to address this knowledge gap through direct measurements of rotor-shaft power and shore-side voltage from a 1 MW, rated at grid-connection, tidal turbine (Orkney Islands, UK). Tidal asymmetry in turbulence parameters, flow speed and power variability were observed. Variability in the power at 0.5 Hz, associated with the 10-min running mean, was low (standard deviation 10–12% of rated power), with lower variability associated with higher flow speed and reduced turbulence intensity. Variability of shore-side measured voltage was well within acceptable levels (∼0.3% at 0.5 Hz). Variability in turbine power had 2 = 85% and ∼14% error). Therefore, the predictability and quality of tidal-stream energy was high and may be undervalued in a future, high-penetration renewable energy, electricity grid.

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Addressing GHG emissions from industry is vital to achieving decarbonisation targets. However, finding alternatives to many industrial energy requirements remains a challenge. Many processes in the food sector require heat at relatively low temperatures (i.e. 80°C to 200°C). High temperature heat pumps under development present a heat source that is efficient (especially if coupled with waste heat sources) and low carbon (especially if powered by decarbonised electricity). This study analysed their potential in the UK Dairy sub-sector and extrapolates this to the wider Food and Drink sector. There is potential to save approximately 164 kt-CO2/yr in the modelled processes. Applied to similar processes across the Food and Drink sector, there is scope to save 2.6 Mt-CO2/yr with projected 2030 grid electricity emissions factors. High temperature heat pumps have the potential to save energy and reduce GHG emissions. These GHG savings will increase further as the electrical grid continues to be decarbonised. While fuel cost savings are possible, these depend upon the processes and become more significant with projected fuel prices. UK Engineering and Physical Sciences Research Council

[EN] The use of pump working as turbine (PAT) was identified by many researchers as a way to improve the energy efficiency in the water systems. However, the majority of the researches consider the hydraulic machine connected to the electrical grid, which may not fit best when these recovery systems are located in rural or remote areas. To improve the efficiency in these recovery systems for rural areas, this research contributes for a further study and optimization of the off-grid PAT systems with induction generators. The current manuscript proposes a methodology to obtain the best efficiency of the PAT-SEIG (Self-Excited Induction Generator) system when operating under different speeds and loads. For these systems, the selection of capacitors for the SEIG is critical to maximizing the energy efficiency. A methodology is proposed to estimate and select the correct SEIG model parameters and, thus, compute the best capacitor values to improve the PAT-SEIG energy efficiency. Special attention is given to the impact the SEIG parameters have in the efficiency of the recovery system. The accuracy of the analytical model improved, reducing the error between analytical and experimental results from 50.8% (for a model with constant parameters) to 13.2% (with parameters changing according to the operating point of the system). These results showed an increase of the overall PAT system efficiency from 26% to 40% for the analyzed case study., This work was supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2019 and the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA PROGRAMME 2014-2020 and CERIS (CEHIDRO-IST), the Hydraulic Laboratory, for experiments on PATs.

For stable operation of the electrical grid, it is vital to maintain a balance between demand and supply of electrical power. Imbalance at any instant between consumption and generation causes voltage and frequency instability. Intermittent generation (wind and solar) in power systems is more likely to cause such imbalances hence the existence of frequency and voltage variations. To address stability issues due to integration of intermittent renewable sources into the grid, a storage device is required which can quickly respond to the power fluctuations. A Flywheel Energy Storage System (FESS) has the capability to respond within a sub-second timescale and is able to address the problems caused by power variations. The performance of FESS is highly dependent on the type of motor/generator (MG) set which is the key component generating or absorbing power from grid. The three main types of electrical machines used in FESS applications are synchronous machine (SM), induction machine (IM) and the switched reluctance machine (SRM). SRM is less commonly used due to high current ripples and complex torque control [1]. SM is used for high speed applications due to its high efficiency and IM is used for high power applications due to its rough construction. This research focuses on the comparison of synchronous and induction machines used in flywheel energy storage systems for microgrid applications [2]. The operation and controlling schemes of each electrical machine has been described as used in the analysis made in the MATLAB/Simulink environment

International audience; In recent years, the question of the energy consumption of data centers has become more and more important, and several studies raised the possibility of using renewable energy to power them. The intermittent nature of commonly used renewable energy sources is a major drawback of using them directly on-site. In this paper, we present an approach for scheduling batch jobs with due date constraints, which takes into account the availability of the renewable energy to reduce the need of brown energy and therefore running cost. The approach we propose differs from the existing methods by providing a scheduling algorithm agnostic of the electrical infrastructure. A separated system, managing the renewable sources, provides an arbitrary objective function, which is used to guide the scheduling heuristic. We implemented our approach in a data center simulator, and evaluated it by considering a small-scale center powered with solar panels and connected to the electrical grid. The relationship between the flexibility allowed by the user negotiated SLAs and the behavior of the algorithm is studied, and compared to existing approaches from the literature. Our experiments show a reduction of brown energy consumption up to 49% and a cost saving up to 51%, compared to a traditional scheduler unaware of renewable availability.

The smart power grid of the future will utilize waveform level monitoring with sampling rates in the kilohertz range for detailed grid status assessment. To this end, we address the challenge of handling large raw data amount with its quasi-periodical characteristic via lossless compression. We compare different freely available algorithms and implementations with regard to compression ratio, computation time and working principle to find the most suitable compression strategy for this type of data. Algorithms from the audio domain (ALAC, ALS, APE, FLAC & TrueAudio) and general archiving schemes (LZMA, Delfate, PPMd, BZip2 & Gzip) are tested against each other. We assemble a dataset from openly available sources (UK-DALE, MIT-REDD, EDR) and establish dataset independent comparison criteria. This combination is a first detailed open benchmark to support the development of tailored lossless compression schemes and a decision support for researchers facing data intensive smart grid measurements.

[EN] The concept of microgrid has emerged as a feasible answer to cope with the increasing number of distributed renewable energy sources which are being introduced into the electrical grid. The microgrid communication network should guarantee a complete and bidirectional connectivity among the microgrid resources, a high reliability and a feasible interoperability. This is in a contrast to the current electrical grid structure which is characterized by the lack of connectivity, being a centralized-unidirectional system. In this paper a review of the microgrids information and communication technologies (ICT) is shown. In addition, a guideline for the transition from the current communication systems to the future generation of microgrid communications is provided. This paper contains a systematic review of the most suitable communication network topologies, technologies and protocols for smart microgrids. It is concluded that a new generation of peer-to-peer communication systems is required towards a dynamic smart microgrid. Potential future research about communications of the next microgrid generation is also identified., This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant BES-2013-064539.

[EN] The use of pumps working as turbines (PATs) connected to the electric system, in the replacement of pressure reduction valves to reduce the excessive pressure in water distribution networks, have been studied for the last years. The introduction of PATs is very important in the water-energy nexus to promote the increase of the energy savings. As consequence, the majority of the water systems does not have access to the electrical grid and, therefore, the need to study the PATS operation off-grid is necessary. In this line, the novelty of this research is the application and optimization of a PAT in water systems when the recovery solution is off-grid type. To operate correctly, the induction machine requires an external source of reactive power, which is typically provided by the electrical grid. To supply the required reactive power, a bank of capacitors is installed at the machine terminals, so-called self-excited induction generator (SEIG). The analytical model, simulation and experimental works were performed, to analyse the SEIG behaviour. The results were applied in a SEIG-PAT system obtaining the global efficiency of the system for different speeds and loads. The global efficiency decreases 47% when off-grid operation, showing the need to optimize the electrical parameters of the generator to operate as off- grid with acceptable efficiency levels. In this framework, a tuning methodology for the SEIG capacitor bank values was developed to be automatically adjusted according to the operating point of the PAT to maximize its efficiency., This research is supported by Academic program career of the faculty of the Universitat Politecnica de Valencia 2016/2017 in the project "Maximization of the global efficiency in PATs in laboratory facility" and by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013. Besides, the authors wish to thank to the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA PROGRAMME 2014-2020 and CERIS (CEHIDRO-IST), the Hydraulic Laboratory, for the support in the conceptual work and in experiments on PATs.

A study of the Australian National Electricity Market shows that using battery storage in the Australian national electricity grid reduces CO2 emissions by providing further flexibility for the operation of conventional generators and also by decreasing the amount of unused renewable energy. Interestingly, energy storage is more efficient at reducing carbon emissions in the context of higher carbon and/or fuel prices. In addition to reducing emissions, battery storage can decrease the cost of delivered energy.

The feasibility of heating sector integration into future highly renewable electrical grid is examined for a regional and pan-European network. A novel geographical weather dependent model for calculating the heat demand using a temporal resolution of an hour with a spatial resolution of 40 × 40 km 2 and an optimized solution for the utilization of excess renewable generation with least energy needs is presented. Heating sector is modeled and coupled separately with two different heat coupling models, heat-pump coupling and electric-resistance coupling, both having heat-storage and gas-boiler. Results show coupling with the regional network requires least heat-storage capacity and coupling with an individual country network requires the least gas-boiler capacity. However, coupling with the pan-European network results in least balancing energy needs. It is found that heat-pump coupling provides more benefit than the electric-resistance coupling, with 4 times more heat-storage energy and 38% less requirement for the gas-boiler energy. Optimum energy mix between the heat-storage energy and gas-boiler energy suggests that the present amount of excess generation is not enough to fully support the heating sector, but if the renewable energy generation is increased by 50% then heat-storage will play an important role.

This chapter introduces the readers to types of natural disasters, their impact on electrical grid, causes, and cost of outages to the world and US economy. The need of energy in the aftermath of disasters both in developed and developing countries is discussed. Energy need in remote and off grid areas is also discussed. The chapter also discusses using photovoltaic energy for disaster relief and remote areas. The chapter concludes by describing the increased use of photovoltaic in the world, and the growth and forecast of photovoltaic markets.

FHRs are a Generation IV fluoride salt-cooled, solid-fueled, high-temperature reactor class currently under development in the United States and China, by both research institutions and universities. The concept of an FHR was generated by trying to integrate advantages of other reactor classes (low pressure, high temperature, high efficiency) while eliminating some of their drawbacks (huge containment domes, low efficiencies). Fixed fuel and pebble fuel, SMRs, and gigawatt reactors are being studied. FHRs are a promising reactor technology for the needs of a future electrical grid with highly fluctuating solar and wind power. FHRs might also serve as a stepping stone to liquid fuel molten salt reactors, helping to understand phenomenology and engineering issues prior to having fuel and associated fission products present in the primary coolant.

Generation units based on renewable energy technologies such as solar, wind, hydro, biomass, etc., have rapidly penetrated into the electrical grid. Today, they constitute a significant percentage of the installed generation capacity and are considered to be an important energy storage option for future generation systems. However, the intermittent and fluctuating nature of renewable generation has led to concerns regarding the stability of an electric grid that derives a large fraction of its energy from these sources and the costs associated with integrating renewables at a large scale into an existing grid. Energy storage technologies offer a possible alternative to overcome some of these challenges by balancing the instantaneous mismatch in supply and demand in the electric grid effectively and thereby improving the system performance. The chapter is intended to offer a broad insight into different electrical energy storage technologies, their applications, challenges to widespread deployment, and future trends and outlooks.

One of the best applications of wind and solar power is in remote locations. Sufficient data and reports are available to estimate benchmark costs for wind applications, and these benchmark costs show that these renewable energy applications tend to be more costly than grid power. Also, backup power is needed and can cost more than the wind or solar systems. However, there are great opportunities and paths forward that will lead to a resurgence of off-grid power in the United States and a dominance of off-grid power in countries where an electrical grid structure is not already in place

Phasor measurement units (PMUs) provide great potential for monitoring an electrical grid by recording synchronized measurements across a wide area. However, with more and more PMUs being deployed, the traditional workflow of processing these data is facing a major challenge. In this chapter, we present our experience in analyzing and mining PMU data for event classification and anomaly detection. Experiments have been carried out using real PMU data that are collected from the Bonneville Power Administration’s power grid. Experimental results show that our methods are effective in identifying both anomalies and known events from a large amount of data. Particularly, our classification method can accurately detect an event from a PMU that is far from the location where the event occurs.

Electricity brings with it diversity in energy source. Diversity brings with it stability in supply and price as well as options for sustainability. To the advantage of electricity, it is able to meet most of the energy demands of society, including displacing of much of jet travel with a new mode of transportation.

The Generation IV program history, evolution, and status in the United States are surveyed, focusing on reactor designs, power units, and related conceptual developments for nuclear power technologies. The broad range of system aspects of the Generation IV program specific to the US energy industry are discussed. The potential role for emerging Generation IV systems is evaluated accounting for their ability to provide electricity and process heat products as well as to serve as one of the potential waste management solutions. Electrical grid integration and options for the US market penetration are included.

In recent years ever more examples of regions that have managed to achieve or orientate themselves toward renewable energy sufficiency are emerging. However, actions to create energy autonomy are mainly the result of isolated activities and they are less driven from fully organized movements. In addition, total energy independence without the support of a centralized electrical grid is yet to be achieved. The objectives of this work are to investigate the associated costs of stand-alone renewable hybrid power plants on a Greek island and compare them to the cost of the currently used fossil-fuel-based conventional plant. The plants examined here are designed to fully cover the electricity needs of the island. Islands may face numerous energy problems and rely heavily on foreign and environmentally-harmful fuels. It is shown that the relatively high cost of electricity of such a remote region can increase the competitiveness and promote the wider incorporation of technologies based on renewable energy sources that may, in other cases, seem economically inferior to business-as-usual energy solutions. Fontina Petrakopoulou would like to thank the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration (grant agreements no 600371 and 332028), the Ministerio de Economía y Competitividad (COFUND2014-51509) the Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

With the increased integration of renewable energy sources the interaction between energy producers and consumers has become a bi-directional exchange. Therefore, the electrical grid must be adapted into a smart grid which effectively regulates this two-way interaction. With the aid of simulation, stakeholders can obtain information on how to properly develop and control the smart grid. In this paper, we present the development of an integrated smart grid simulation model, using the Anylogic simulation environment. Among the elements which are included in the simulation model are houses connected to a renewable energy source, and batteries as storage devices. With the use of the these elements a neighbourhood model can be constructed and simulated under multiple scenarios and configurations. The developed simulation environment provides users better insight into the effects of running different configurations in their houses as well as allow developers to study the inter-exchange of energy between elements in a smart city on multiple levels.

Combined heat and power on a residential scale, also known as micro-cogeneration, is currently gaining traction as an energy savings practice. The configuration of micro-cogeneration systems is highly variable, as local climate, energy supply, energy market and the feasibility of including renewable type components such as wind turbines or photovoltaic panels are all factors. Large-scale lithium ion batteries for electrical storage in this context can provide cost savings, operational flexibility, and reduced stress on the distribution grid as well as a degree of contingency for installations relying upon unsteady renewables. Concurrently, significant advances in component materials used to make lithium ion cells offer performance improvements in terms of power output, energy capacity, robustness and longevity, thereby enhancing their prospective utility in residential micro-cogeneration installations. The present study evaluates annual residential energy use for a typical Canadian home connected to the electrical grid, equipped with a micro-cogeneration system consisting of a Stirling engine for supplying heat and power, coupled with a nominal 2 kW/6 kW h lithium ion battery. Two novel battery cathode chemistries, one a new Li–NCA material, the other a high voltage Ni-doped lithium manganate, are compared in the residential micro-cogeneration context with a system equipped with the presently conventional LiMn2O4 spinel-type battery. Fil: Darcovich, K.. National Research Council of Canada; Canadá Fil: Henquín, Eduardo Rubén. National Research Council of Canada; Canadá. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina Fil: Kenney, B.. National Research Council of Canada; Canadá Fil: Davidson, I. J.. National Research Council of Canada; Canadá Fil: Saldanha, N.. Carleton University; Canadá Fil: Beausoleil Morrison, I.. Carleton University; Canadá

Process industry supply chains have been an active area of research for decades with advances towards decentralized management and development of closed loop resilience. Along similar lines, now electric power systems are moving towards smart grids that allow for decentralized power generation. At the surface level there are many differences between electrical grids and supply chains; for example, there is limited/no storage in case of electric power, hence supply and demand has to be matched at every time instant. However, in the recent times it has been recognized that some of the control and enterprise-wide optimization strategies that are an integral part of supply chain management have analogues in power systems as well. This article seeks to systematically identify the structural similarities and differences between these hitherto unrelated domains. We will illustrate these using a case study of Indian electrical grid.

Experiences from smart grid cyber-security incidents in the past decade have raised questions on the applicability and effectiveness of security measures and protection mechanisms applied to the grid. In this chapter we focus on the security measures applied under real circumstances in today’s smart grid systems. Beginning from real world example implementations, we first review cyber-security facts that affected the electrical grid, from US blackout incidents, to the Dragonfly cyber-espionage campaign currently focusing on US and European energy firms. Provided a real world setting, we give information related to energy management of a smart grid looking also in the optimization techniques that power control engineers perform into the grid components. We examine the application of various security tools in smart grid systems, such as intrusion detection systems, smart meter authentication and key management using Physical Unclonable Functions, security analytics and resilient control algorithms. Furthermore we present evaluation use cases of security tools applied on smart grid infrastructure test-beds that could be proved important prior to their application in the real grid, describing a smart grid intrusion detection system application and security analytics results. Anticipated experimental results from the use-cases and conclusions about the successful transitions of security measures to real world smart grid operations will be presented at the end of this chapter.

Electricity transmission and distribution (T&D) plays a vital role in society by connecting electricity producers and consumers. We present a life cycle assessment case study of electricity delivery to consumers in Nord-Trøndelag county in Norway. We use a coherent framework for assessing electricity transfer via all the main segments of the Norwegian T&D system (local distribution, regional transmission and main national transmission grids). The assessment covers impacts associated with production, transport, and installation of components, power grid losses, and losses of sulphur hexafluoride. The results indicate that for electricity that is transmitted through the three main T&D grid segments, and assuming a Norwegian electricity mix when modelling the effects of power losses, the total carbon footprint of electricity T&D is 7.8 kg CO2-eq/MW h. Local distribution holds the largest share of this total (∼60%), while regional transmission and national transmission both make smaller but significant contributions (∼20% each). When classifying impacts as being attributable to either power grid losses or to other processes (e.g., materials and component manufacturing), both power losses and other processes contribute significantly to total impact potentials. Power losses are responsible for 30–43% of the combined electricity T&D impact potentials for climate change, particulate matter, smog-creation and acidification, 21–28% for toxicity and eutrophication, and 14% for metal depletion. For all categories except metal depletion, the relative importance of power losses increases appreciably if Nordic or particularly European electricity is assumed, however. Finally, we compare the environmental impacts of electricity T&D with that of electricity generation. The results of the comparison show that electricity T&D causes fewer impacts than electricity generation, but T&D impacts are not negligible; this is true regardless of what electricity mix is assumed when modelling power losses.

This chapter examines the manner in which CO2-derived fuels, synthesised using renewable energy, can help with decarbonisation of several sectors. Three areas are discussed: the electrical generation system, transport fuels and energy for heating. In addition, the advantages and disadvantages of such CO2-derived fuels are considered with respect to other energy storage technologies. An introduction to the variations in daily demand on an electrical grid is illustrated by considering a single day experienced by the Spanish grid and the concept of a “generation mix” is presented. Synthetic fuels for transportation are explored alongside alternatives to the internal combustion engine, such as fuel cells and batteries. Finally, the area of heat provision, where CO2-derived fuels can be expected to play a vital role is introduced.

In the last years, electric vehicles (EVs) are getting significant consideration as an environmental-sustainable and cost-effective alternative over conventional vehicles with internal combustion engines (ICEs), for the mitigation of the dependence from fossil fuels and for reduction of Green-House Gasses (GHGs) emission. However, many challenges are still ongoing to their large scale implementation. Among them, the negative impact on the electrical grid operation in case of an uncoordinated contemporary charging of a huge number of EVs. In the recent literature different solutions are proposed for handling the peak demand of EVs and the related problems. One answer is offered by the implementation of EV charging strategies, through aggregation agents, for containing the impact on the grid, guaranteeing the quality of the service. The implementation of a real charging strategy is strictly related to a deployment of smart-grid technologies, such as smart meters, Information and Communication Technologies (ICTs) and energy storage systems (ESSs). In particular ESSs are playing a fundamental role in the general smart grid paradigm, and can become fundamental for the integration in the new power systems of EV fast charging stations of the last generation: in this case the storage can have peak shaving and power quality functions and also to make the charge time shorter. In the present paper, an overview on the different types of EVs charging stations, in reference to the present international European standards, and on the storage technologies for the integration of EV charging stations in smart grid is reported. Then a real implementation of EVs fast charging station equipped with an ESS is deeply described. The system is a prototype, designed, implemented and now available at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) labs. A wide experimental activity has been performed on the prototype system in order to test its functionalities in the integration in a smart grid available at the same ENEA lab, including a smart metering system. The integration has been possible thanks to the use of a customized communication protocol, developed by the researchers and here described. The results of the experimental tests show that the system has a good performance in the implementation of peak shaving functions, in respect of the main distribution grid, making the prototype like a network nearly zero-impact system. © 2014 Elsevier B.V. All rights reserved.

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed. The financial support from EPSRC for Liz Varga on project entitled "Transforming Utilities’ Conversion Points" (no. EP/J005649/1) is gratefully acknowledged.

The process of connecting a small wind turbine to an electric utility has two different but necessary components. One is policy and permitting; the other is the actual physical connection. The former requires all the necessary paperwork for permission to connect, and the latter requires overcoming technical obstacles and making sure that the connection is dependable and safe. Each electric utility can establish its own policies and requirements for connecting to its grid. Several typical methods of metering the electricity are presented and suggestions for maximizing the wind-generated electricity are given.

Most small wind machines were operated as stand-alone systems prior to 1970, when electrical generating machines started being connected to the utility grid. For centuries, small wind systems have been used for pumping water and grinding grain. Almost all of the wind systems sold that are less than 1 kW in size are used for battery charging; they are used in homes, on boats, for outdoor security lighting, and for navigational lighting. Water pumping remains a large user of small mechanical wind systems. Hybrid wind systems used in remote villages and islands are included in this discussion.

This paper investigates hydrogen and methane generation from large hydraulic plant, using an original multilevel thermo-economic optimization approach developed by the authors. Hydrogen is produced by water electrolysis employing time-dependent hydraulic energy related to the water which is not normally used by the plant, known as “spilled water electricity”. Both the demand for spilled energy and the electrical grid load vary widely by time of year, therefore a time-dependent hour-by-hour one complete year analysis has been carried out, in order to define the optimal plant size. This time period analysis is necessary to take into account spilled energy and electrical load profiles variability during the year. The hydrogen generation plant is based on 1 MWe water electrolysers fuelled with the “spilled water electricity”, when available; in the remaining periods, in order to assure a regular H 2 production, the energy is taken from the electrical grid, at higher cost. To perform the production plant size optimization, two hierarchical levels have been considered over a one year time period, in order to minimize capital and variable costs. After the optimization of the hydrogen production plant size, a further analysis is carried out, with a view to converting the produced H 2 into methane in a chemical reactor, starting from H 2 and CO 2 which is obtained with CCS plants and/or carried by ships. For this plant, the optimal electrolysers and chemical reactors system size is defined. For both of the two solutions, thermo-economic optimization results are discussed and compared with particular emphasis to energy scenario, economic aspects, system size, capital costs and related investments. It is worth noting that the results reported here for this particular large H 2 plant case represents a general methodology, since it can vary according to their different sizes, primary renewable energy, plant location, and different H 2 utilization.

A microgrid (MG) is a local energy system consisting of a number of energy sources, energy storage units and loads that operate connected to the main electrical grid or autonomously. MGs include wind, solar or other renewable energy sources. MGs provide flexibility, reduce the main electricity grid dependence and contribute to change the large centralized production paradigm to local and distributed generation. However, such energy systems require complex management, advanced control and optimization. Interest on MGs hierarchical control has increased due to the availability of cheap online measurements. Similarly to any process system, MG hierarchical control is divided into three levels. However, an additional control algorithm is required to manage power transmission between sources and loads, maximizing efficiency and minimizing transmission losses. This real-time optimization problem is addressed to locally readjust converters operation to attain global efficiency. An algorithm is presented by formulating and solving the power sharing optimization problem in a two-level approach. The objective function is the sum of the apparent power transferred, whose minimization reduces total power losses and energy costs. The performance of the approach proposed is validated on a simulated case study. Different scenarios are tested and the performance of the algorithm is compared and discussed. The power losses reduction obtained with the proposed approach are compared with those obtained by standard procedures (Equal Power Sharing - EPS), showing enhanced performance.

Publisher Summary Smart grids utilize communication technology and information to optimally transmit and distribute electricity from suppliers to consumers. Smart grid is not a static concept; it continues to evolve as the existing technologies evolve and new technologies are developed. The type, configuration, and implementation of these technologies and the access to and transmission and use of relevant information are of primary concern in securing smart grids. To achieve the desired goals of reliable, efficient, and clean energy distribution, smart grids employ a combination of different technologies. The technologies are integrated two-way communication, advanced components, advanced control methods, sensing and measurement technologies, improved interfaces and decision support, and applications of smart grid technology. Smart grid promises increased efficiency, reliability, and a more economical means of distributing and transmitting power. These improvements rely on new technologies and new levels of interconnectivity built into the electrical grid, as well as cooperation among different organizations and analysis of massive amounts of data.

This chapter focuses on the state of development of batteries for plug-in hybrid electric vehicles (PHEVs). The battery plays a crucial role in the PHEV architecture by storing energy from the electrical grid and from the gasoline engine, through a generator, as well as passing energy back and forth with the electric motor to maximize efficiency. The commercial success of the PHEV depends on the development of appropriate battery technologies and there is much uncertainty about the requirements of a battery required for a successful PHEV and where the present battery technologies stand in meeting such requirements. The basic design concepts of PHEVs are discussed and the three sets of influential technical battery goals are compared. The inherent trade-offs in PHEV battery design are also explained and the current state of several battery chemistries along with the comparison of their abilities to meet PHEV goals and their potential trajectories for further improvement are also presented. PHEV battery goals may vary according to differing assumptions of PHEV design, performance, use patterns, and consumer demand and the battery development is still constrained by inherent trade-offs among five main battery attributes such as power, energy, longevity, safety, and cost. The findings suggest that lithium–ion (Li–ion) battery designs are better suited to meet the demands of more aggressive PHEV goals than nickel-metal hydride (NiMH) batteries, which are currently used for HEVs. The flexible nature of Li–ion technology, as well as concerns over safety, has also prompted several alternate paths of continued technological development.

It is reasonable to speculate that most electrochemical power sources will supply electrical power to loads at residential and commercial voltage levels. A few, however, may interface at higher distribution voltage levels, which are much higher than commercial/residential voltages. In either case, only qualified personnel should have access to the energized wiring and related apparatus; both residential/commercial and distribution voltage levels are potentially harmful to humans. This article presents a brief description of the electrical environment that electrochemical power sources are most likely to interface and the attendant safety implications. In many applications, these power sources will supplement the electrical grid as a backup or standby power supply. In others, they may support isolated loads that are remote from any alternative source of electricity. The former, for example, may be electrochemical batteries that take charge from the grid and return power to the grid, both at opportune times and under opportune circumstances. The latter may be fuel cells that generate electrical power from primary resources to support remote, isolated loads. In either case, the output voltage is most likely to be a standard, grid-supplied voltage so as to accommodate loads designed to interface with standard electrical grids.

Publisher Summary This case study presents a risk assessment of a hypothetical terrorist attack on a portion of the national electrical grid. It presents a scoping analysis to examine the need and value added of such an assessment. The goal is to obtain insights on the risk of a terrorist attack of a specific region of the electrical grid serving several large metropolitan areas. Risk management is an integral part of the electricity sector's program of “critical infrastructure protection.” As such, these efforts are defined as “safeguarding the essential components of the electric infrastructure against physical and electronic threats in a manner consistent with appropriate risk management, with both industry and industry–government partnerships, while sustaining public confidence in the electricity sector.” This case study involves a risk assessment of a combined cyber and physical attack on a hypothetical electric power grid. The threat assessment part of the case study is more limited than the vulnerability assessment; this is primarily due to security considerations and a lack of resources to examine classified threat information.