Abstract: What does it mean to achieve a 100% renewable grid? Several countries already meet or come close to achieving this goal. Iceland, for example, supplies 100% of its electricity needs with either geothermal or hydropower. Other countries that have electric grids with high fractions of renewables based on hydropower include Norway (97%), Costa Rica (93%), Brazil (76%), and Canada (62%). Hydropower plants have been used for decades to create a relatively inexpensive, renewable form of energy, but these systems are limited by natural rainfall and geographic topology. Around the world, most good sites for large hydropower resources have already been developed. So how do other areas achieve 100% renewable grids? Variable renewable energy (VRE), such as wind and solar photovoltaic (PV) systems, will be a major contributor, and with the reduction in costs for these technologies during the last five years, large-scale deployments are happening around the world.

Abstract: The clean energy transition requires a co-evolution of innovation, investment, and deployment strategies for emerging energy storage technologies. A deeply decarbonized energy system research platform needs materials science advances in battery technology to overcome the intermittency challenges of wind and solar electricity. Simultaneously, policies designed to build market growth and innovation in battery storage may complement cost reductions across a suite of clean energy technologies. Further integration of R&D and deployment of new storage technologies paves a clear route toward cost-effective low-carbon electricity. Here we analyse deployment and innovation using a two-factor model that integrates the value of investment in materials innovation and technology deployment over time from an empirical dataset covering battery storage technology. Complementary advances in battery storage are of utmost importance to decarbonization alongside improvements in renewable electricity sources. We find and chart a viable path to dispatchable US$1 W−1 solar with US$100 kWh−1 battery storage that enables combinations of solar, wind, and storage to compete directly with fossil-based electricity options. Electricity storage will benefit from both R&D and deployment policy. This study shows that a dedicated programme of R&D spending in emerging technologies should be developed in parallel to improve safety and reduce overall costs, and in order to maximize the general benefit for the system.

Abstract: The invention and application of the electric power technology triggered the second industrial revolution in human history, which marked the human society entered the age of electricity. Electricity provides the sustainable power for economic and social development. With the rapid development of economy, the electricity consumption is also increasing. The increase of electricity consumption has further promoted the progress of the industrial economy. In order to achieve the goal of improving the level of economic development while reducing energy consumption, it is necessary to reveal the relationship between electricity consumption and economic growth. This study is an extensive overview of the literature surrounding this topic. In this paper, we focus on the relationship between electricity consumption and economic growth in China. We first analyze the general situation of China's electricity consumption and economic development. Then we explore the relationship between China's electricity consumption and economic growth from three dimensions, i.e., the time dimension, the regional dimension and the industrial dimension. Finally, we study the key issues in the research of the relationship between electricity consumption and economic growth, including variable selection, model construction and results discussion. This work suggests that the nature of the nexus in China should and can be explored from a wider perspective, by developing a suitable integrated methodological framework.

Abstract: Electricity load forecasting is an important tool which can be utilized to enable effective control of commercial building electricity loads. Accurate forecasts of commercial building electricity loads can bring significant environmental and economic benefits by reducing electricity use and peak demand and the corresponding GHG emissions. This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models. A comparison between the models is then presented for forecasting day ahead hourly electricity loads using real building and Campus data obtained from the Kensington Campus and Tyree Energy Technologies Building (TETB) at the University of New South Wales (UNSW). The results reveal that Artificial Neural Networks with Bayesian Regulation Backpropagation have the best overall root mean squared and mean absolute percentage error performance and almost all the models performed better predicting the overall Campus load than the single building load. The models were also tested on forecasting daily peak electricity demand. For each model, the obtained error for daily peak demand forecasts was higher than the average day ahead hourly forecasts. The regression models which were the main focus of the study performed fairly well in comparison to other more advanced machine learning models.

Abstract: The Well-To-Wheels (WTW) methodology is widely used for policy making in the transportation sector. In this paper updated WTW calculations are provided, relying on 2013 statistic data, for the carbon intensity (CI) of the European electricity mix; detail is provided for electricity consumed in each EU Member State (MS). An interesting aspect presented is the calculation of the GHG content of electricity traded between Countries, affecting the carbon intensity of the electricity consumed at national level. The amount and CI of imported electricity is a key aspect: a Country importing electricity from another Country with a lower CI of electricity will lower, after the trade, its electricity CI, while importing electricity from a Country with a higher CI will raise the CI of the importing Country. In average, the CI of electricity used in EU at low voltage in 2013 was 447 gCO2eq/kWh, which is the 17% less compared to 2009. Then, some examples of calculation of GHG emissions from the use of electric vehicles (EVs) compared to internal combustion engine vehicles are provided. The use of EVs instead of gasoline vehicles can save (about 60% of) GHG in all or in most of the EU MSs, depending on the estimated consumption of EVs. Compared with diesel, EVs show average GHG savings of around 50% and not savings at all in some EU MS.

Abstract: Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.

Abstract: Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.

Abstract: The extensive installation of gas-fired power plants in many parts of the world has led electric systems to depend heavily on reliable gas supplies. The use of gas-fired generators for peak load and reserve provision causes high intraday variability in withdrawals from high-pressure gas transmission systems. Such variability can lead to gas price fluctuations and supply disruptions that affect electric generator dispatch, electricity prices, and threaten the security of power systems and gas pipelines. These infrastructures function on vastly different spatio-temporal scales, which prevents current practices for separate operations and market clearing from being coordinated. In this paper, we apply new techniques for control of dynamic gas flows on pipeline networks to examine day-ahead scheduling of electric generator dispatch and gas compressor operation for different levels of integration, spanning from separate forecasting, and simulation to combined optimal control. We formulate multiple coordination scenarios and develop tractable physically accurate computational implementations. These scenarios are compared using an integrated model of test networks for power and gas systems with 24 nodes and 24 pipes, respectively, which are coupled through gas-fired generators. The analysis quantifies the economic efficiency and security benefits of gas-electric coordination and dynamic gas system operation.

Abstract: In this paper, a new feature selection and forecast engine is presented for day ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, non-stationary, and time variance. Also, an appropriate feature selection is crucial for accurate forecasting. In this paper, a two-step approach that identifies a set of candidate features based on the data characteristics proposed and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way. Then, a combination of wavelet transform (WT) and a hybrid forecast method is presented based on neural network (NN) and an optimization algorithms. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. These comparisons illustrate effectiveness of the proposed

Abstract: This paper aims to describe the role of advanced sensing systems in the electric grid of the future. In detail, the project, development, and experimental validation of a smart power meter are described in the following. The authors provide an outline of the potentialities of the sensing systems and IoT to monitor efficiently the energy flow among nodes of an electric network. The described power meter uses the metrics proposed in the IEEE Standard 1459–2010 to analyze and process voltage and current signals. Information concerning the power consumption and power quality could allow the power grid to route efficiently the energy by means of more suitable decision criteria. The new scenario has changed the way to exchange energy in the grid. Now, energy flow must be able to change its direction according to needs. Energy cannot be now routed by considering just only the criterion based on the simple shortening of transmission path. So, even energy coming from a far node should be preferred, if it has higher quality standards. In this view, the proposed smart power meter intends to support the smart power grid to monitor electricity among different nodes in an efficient and effective way.

Abstract: Distributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work

Abstract: In the literature electric vehicle use is modelled using of a variety of approaches in power systems, energy and environmental analyses as well as in travel demand analysis. This paper provides a systematic review of these diverse approaches using a twofold classification of electric vehicle use representation, based on the time scale and on substantive differences in the modelling techniques. For time of day analysis of demand we identify activity-based modelling (ABM) as the most attractive because it provides a framework amenable for integrated cross-sector analyses, required for the emerging integration of the transport and electricity network. However, we find that the current examples of implementation of AMB simulation tools for EV-grid interaction analyses have substantial limitations. Amongst the most critical there is the lack of realism how charging behaviour is represented.

Abstract: In September 2015, the United Nations General Assembly adopted Agenda 2030, which comprises a set of 17 Sustainable Development Goals (SDGs) defined by 169 targets. 'Ensuring access to affordable, reliable, sustainable and modern energy for all by 2030' is the seventh goal (SDG7). While access to energy refers to more than electricity, the latter is the central focus of this work. According to the World Bank's 2015 Global Tracking Framework, roughly 15% of the world's population (or 1.1 billion people) lack access to electricity, and many more rely on poor quality electricity services. The majority of those without access (87%) reside in rural areas. This paper presents results of a geographic information systems approach coupled with open access data. We present least-cost electrification strategies on a country-by-country basis for Sub-Saharan Africa. The electrification options include grid extension, mini-grid and stand-alone systems for rural, peri-urban, and urban contexts across the economy. At low levels of electricity demand there is a strong penetration of standalone technologies. However, higher electricity demand levels move the favourable electrification option from stand-alone systems to mini grid and to grid extensions.

Abstract: Pakistan is facing severe electricity supply shortages, causing forced power outages over the last decade ranging from 8 to 12 h a day in urban areas and up to 18 h in rural areas. The major causes behind the increasing gap between supply and demand are mainly increases in electricity demand on one hand, and depleting energy resources and financial constraints on the other. In this context the government has been taking various measures, including a partial restructuring of the electricity sector under guidelines from international financing institutes. At present, the country is, therefore, not only facing a serious challenge of meeting the electricity demand but is also facing the challenge of ensuring energy security in the context of globally important climate change issues. The role of policy makers at this stage is crucial for not only assessing and reviewing the current strategies to minimize the electricity supply and demand gap, but also the need to develop future strategies, ensuring affordable electricity with efficient generation, transmission and distribution towards sustainable development in the country. This paper provides a review of the current energy situation, discussion on policy drivers and plans, analysis of the issues of the power sector as well as recommendations for narrowing the electricity supply-demand gap towards sustainable electricity for the country.

Abstract: A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.

Abstract: This paper proposes a novel demand response method that aims at reducing the long-term cost of charging the battery of an individual plug-in electric vehicle (PEV). The problem is cast as a daily decision-making problem for choosing the amount of energy to be charged in the PEV battery within a day. We model the problem as a Markov decision process (MDP) with unknown transition probabilities. A batch reinforcement-learning (RL) algorithm is proposed for learning an optimum cost-reducing charging policy from a batch of transition samples and making cost-reducing charging decisions in new situations. In order to capture the day-to-day differences of electricity charging costs, the method makes use of actual electricity prices for the current day and predicted electricity prices for the following day. A Bayesian neural network is employed for predicting the electricity prices. For constructing the RL training dataset, we use historical prices. A linear-programming-based method is developed for creating a dataset of optimal charging decisions. Different charging scenarios are simulated for each day of the historical time frame using the set of past electricity prices. Simulation results using real-world pricing data demonstrate cost savings of 10%–50% for the PEV owner when using the proposed charging method.