Development of Grid e-Infrastructure in South-Eastern Europe

Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure. It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe. From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries. During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries. Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure. In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.Comment: 22 pages, 12 figures, 4 table

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Hawaii Utility Integration Initiatives to Enable Wind (Wind HUI) Final Technical Report

To advance the state and nation toward clean energy, Hawaii is pursuing an aggressive Renewable Portfolio Standard (RPS), 40% renewable generation and 30% energy efficiency and transportation initiatives by 2030. Additionally, with support from federal, state and industry leadership, the Hawaii Clean Energy Initiative (HCEI) is focused on reducing Hawaii's carbon footprint and global warming impacts. To keep pace with the policy momentum and changing industry technologies, the Hawaiian Electric Companies are proactively pursuing a number of potential system upgrade initiatives to better manage variable resources like wind, solar and demand-side and distributed generation alternatives (i.e. DSM, DG). As variable technologies will continue to play a significant role in powering the future grid, practical strategies for utility integration are needed. Hawaiian utilities are already contending with some of the highest penetrations of renewables in the nation in both large-scale and distributed technologies. With island grids supporting a diverse renewable generation portfolio at penetration levels surpassing 40%, the Hawaiian utilities experiences can offer unique perspective on practical integration strategies. Efforts pursued in this industry and federal collaborative project tackled challenging issues facing the electric power industry around the world. Based on interactions with a number of western utilities and building on decades of national and international renewable integration experiences, three priority initiatives were targeted by Hawaiian utilities to accelerate integration and management of variable renewables for the islands. The three initiatives included: Initiative 1: Enabling reliable, real-time wind forecasting for operations by improving short-term wind forecasting and ramp event modeling capabilities with local site, field monitoring; Initiative 2: Improving operators situational awareness to variable resources via real-time grid condition monitoring using PMU devices and enhanced grid analysis tools; and Initiative 3: Identifying grid automation and smart technology architecture retrofit/improvement opportunities following a systematic review approach, inclusive of increasing renewables and variable distributed generation. Each of the initiative was conducted in partnership with industry technology and equipment providers to facilitate utility deployment experiences inform decision making, assess supporting infrastructure cost considerations, showcase state of the technology, address integration hurdles with viable workarounds. For each initiative, a multi-phased approach was followed that included 1) investigative planning and review of existing state-of-the-art, 2) hands on deployment experiences and 3) process implementation considerations. Each phase of the approach allowed for mid-course corrections, process review and change to any equipment/devices to be used by the utilities. To help the island grids transform legacy infrastructure, the Wind HUI provided more systematic approaches and exposure with vendor/manufacturers, hand-on review and experience with the equipment not only from the initial planning stages but through to deployment and assessment of field performance of some of the new, remote sensing and high-resolution grid monitoring technologies. HELCO became one of the first utilities in the nation to install and operate a high resolution (WindNet) network of remote sensing devices such as radiometers and SODARs to enable a short-term ramp event forecasting capability. This utility-industry and federal government partnership produced new information on wind energy forecasting including new data additions to the NOAA MADIS database; addressed remote sensing technology performance and O&M (operations and maintenance) challenges; assessed legacy equipment compatibility issues and technology solutions; evaluated cyber-security concerns; and engaged in community outreach opportunities that will help guide Hawaii and the nation toward more reliable adoption of clean energy resources. Results from these efforts are helping to inform Hawaiian utilities continue to Transform infrastructure, Incorporate renewable considerations and priorities into new processes/procedures, and Demonstrate the technical effectiveness and feasibility of new technologies to shape our pathways forward. Lessons learned and experience captured as part of this effort will hopefully provide practical guidance for others embarking on major legacy infrastructure transformations and renewable integration projects