CROSS-Fire : a risk management decision support system on the Grid

The CROSS-Fire project aims to develop a grid-based risk management decision support system for the Civil Protection (CP) authorities, using forest fires as the main case study and FireStation as a standalone CAD application to simulate the fire spread over complex topography. CROSS-Fire approach is based in an architecture that includes: information models, encodings, and metadata that represent the scientific knowledge associated to FireStation execution models and standards to enable the discovery and access of Web services, data repository, sensor networks and data processing facilities. To achieve the desired integration of information and services we use: i) EGEE to provide raw technological capability provision, including data management and storage, access to meta-data data bases and high-performance computing and ii) a Geospatial Information Infrastructure based on OCG-WS and SWE Web services to provide the access and management of remote geospatial data and virtualized sensor networks. This article, stresses the relevance of standards adoption of OGC-WS by describing the work that is been done to provide G-FireStation with: i) a standard-based SDI layer, based on Geoserver to exploit/enable geospatial services for data access/processing and ii) a 52N’s implementation of a OGC-SWE compatible layer, to address sensors CP data sources, such as meteorological stations data and satellite images and iii) the development of G-FireStation graphical user interface to access the platform facilities. The core of the CROSS-Fire Platform is a WPS 52North OGC standard layer divided into three interoperable components, respectively, the CROSS-Fire Business Logic, the Grid Services and Geospatial Services. WPS serves as an interface to a wide range of distributed computing resources provides the mechanism to access the grid facilities for processing and data management and including all the algorithms, calculation, or model that operates on spatially referenced data, also mediating all the communication with the portal and other GUI clients. The G-FireStation user interface that is currently under development is an open-source desktop with GIS and CAD capabilities that exploits an SDI client complying with OGC-WS and EU INSPIRE directives. It provides facilities to locate and access the spatial data infrastructure and to visualize the fire propagation, based on the native facilities of gvSig, it was also extended to support a OGC WPS client that mediate all the interactions with the core WPS service layer.Fundação para a Ciência e a Tecnologia (FCT

Strategic decision-making support for distribution system planning with flexibility alternatives

The ongoing power system transformation requires rethinking the planning and operation practices of the different segments to accommodate the necessary changes and take advantage of the forthcoming opportunities. This paper concerns novel approaches for appraising initiatives involving the use of flexibility from grid-connected users. This paper proposes a Decision Theory based Multi-Criteria Cost-Benefit Analysis (DT-MCA-CBA) methodology for smart grid initiatives that capture the complexity of the distribution system planning activities in which flexibility competes with grid expansion. Based on international guidelines, the proposed DT-MCA-CBA methodology systematically assesses tangible and intangible impacts, considering multiple conflicting criteria. The DT-MCA-CBA methodology relies on a novel approach that combines MCA and Decision Theory to identify the most valuable option in a complex decision-making problem by modelling the stakeholder perspective with the MiniMax regret decision rule. The proposed DT-MCA-CBA methodology is applied to a comparative case study concerning four different approaches for distribution system planning. A web-based software which implements the proposed decision-making framework and the DT-MCA-CBA methodology is developed to provide a novel decision-making support tool for strategical smart distribution system planning

Forest accessibility, Madonie mountains (northern Sicily, Italy): implementing a GIS decision support system

Valorisation and sustainable exploitation of woody biomass from cultivation interventions might be an important opportunity to track alternative development trails for rural communities in natural protected areas. The governance of Mediterranean protected areas is characterized by overlapping, sometimes conflicting institutions, stakeholders and regulations, causing negative impacts on decision-making processes. We present an open source GIS-based decision support system tool for mapping forest accessibility and optimizing woody biomass extraction. Two models were implemented to support forest managers during the decision-making process in designing and managing wood-energy supply chains. The optimal grid resolution to run the models was determined via a Least Cost Path analysis. The models were executed at different scales, performing satisfactorily when distances between recorded and modelled paths were lower than the grid unit. The higher the scale, the more the percentile of distances lower than the grid unit. The models were validated in Madonie mountains, Sicily, Italy

Joint Manufacturing and Onsite Microgrid System Control using Markov Decision Process and Neural Network Integrated Reinforcement Learning

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is used to formulate the decision-making model. A neural network integrated reinforcement learning algorithm is proposed to approximately estimate the value function given policy of MDP. A case study based on a manufacturing system as well as a typical onsite microgrid generation system is conducted to validate the proposed MDP model as well as the solution strategy

PRECISION IRRIGATION WITH SENSOR-GRID FOR DECISION SUPPORT SYSTEM

Precision irrigation is the new generation irrigation systems that have tremendous potential to improve water control. This paper presents decision support system for precision irrigation in which sensor-grid technology is used to assist irrigation management decisions. Besides, this paper also explains the definition of precision irrigation and its benefits in irrigation management system. The agriculture is depended on quantity and quality of water. The main problem of water quantity is how to consider the water resources like dam or river, irrigation infrastructure and rain condition. The methodology to establish large-scale remote intelligent irrigation system based on sensor-grid by using wireless sensor network and considering grid climate stations

Pattern-based Automatic Translation of Structured Power System Data to Functional Models for Decision Support Applications

Improved information and insight for decision support in operations and design are central promises of a smart grid. Well-structured information about the composition of power systems is increasingly becoming available in the domain, e.g. due to standard information models (e.g. CIM or IEC61850) or otherwise structured databases. More measurements and data do not automatically improve decisions, but there is an opportunity to capitalize on this information for decision support. With suitable reasoning strategies data can be contextualized and decision-relevant events can be promoted and identified. This paper presents an approach to link available structured power system data directly to a functional representation suitable for diagnostic reasoning. The translation method is applied to test cases also illustrating decision support

Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

Smart grid for a sustainable future

Advances in micro-electro-mechanical systems (MEMS) and information communication technology (ICT) have facilitated the development of integrated electrical power systems for the future. A recent major issue is the need for a healthy and sustainable power transmission and distribution system that is smart, reliable and climate-friendly. Therefore, at the start of the 21st Century, Government, utilities and research communities are working jointly to develop an intelligent grid system, which is now known as a smart grid. Smart grid will provide highly consistent and reliable services, efficient energy management practices, smart metering integration, automation and precision decision support systems and self healing facilities. Smart grid will also bring benefits of seamless integration of renewable energy sources to the power networks. This paper focuses on the benefits and probable deployment issues of smart grid technology for a sustainable future both nationally and internationally. This paper also investigates the ongoing major research programs in Europe, America and Australia for smart grid and the associated enabling technologies. Finally, this study explores the prospects and characteristics of renewable energy sources with possible deployment integration issues to develop a clean energy smart grid technology for an intelligent power system