Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains

—In the last decade, Artificial Intelligence (AI) have been applied overwhelmingly in various research domains in the context of smart grid. It has been one of the main streams of advanced technological approaches that the research community offered for developing smart grids. However, the broad scope of the subject matter launch complexity for scholars to identify effective research approaches. In this paper, we present a literature review about utilizing AI in the key elements of smart grids including grid-connected vehicles, data-driven components, and the power system network. This will result in highlighting technical challenges of the integration of electric vehicles to the grid and the power network operation as well. Moreover, we discuss the four key research areas in the context of AI and its applications in intelligent power grids. The proposed research fields aid PhD candidates to consider these areas as the promising domains for investigation

Identifying Influential Factors Affecting the Shading of a Solar Panel

Photovoltaic (PV) systems produce less energy when operating under shadings. PV planners need to identify important factors affecting the shadings to forecast power generations in various ambient conditions. Using a case study, we show that overlooking the impact of an environmental factor, herein snowfalls, will result in overestimations in the power forecasting. In this paper, we study the context of the shading from different perspectives and introduce parameters that can affect the duration and severity of shading conditions. To identify key notions of the shading and important factors involved, we implement a literature review and include experts’ knowledge by exploring PV planning tools and conducting a survey in the sector of solar energy. The identified factors can be used to develop a knowledge-based model representing key concepts associated with shading conditions. In addition, the identification of important factors affecting the duration and severity of shading conditions addresses new research domains that need to be explored in the field of PV shading and power estimation

Using Software Quality Evaluation Standard Model for Managing Software Development Projects in Solar Sector

This paper proposes a framework for managing Project Quality Management (PQM) processes of software development projects related to photovoltaic (PV) system design. The International Organization for Standardization (ISO) quality evaluation model, Software Product Quality Requirements and Evaluation (SQuaRE) standard, is used to determine quality characteristics and quality metrics of the software. This work presents the following contributions: I) defining quality characteristics associated with a PV design software using the SQuaRE standard model, II) adding the proposed framework as a tool and technique which is used by practitioners following the global standard book for project managers, A Guide to the Project Management Body of Knowledge (PMBOK), and III) Identifying quality measures and sub-characteristics of a PV design software. The presented model can be employed for simulation-based and/or model-based software products in various technical fields and engineering

Proposing an Ontology Model for Planning Photovoltaic Systems

The performance of a photovoltaic (PV) system is negatively affected when operating under shading conditions. Maximum power point tracking (MPPT) systems are used to overcome this hurdle. Designing an efficient MPPT-based controller requires knowledge about power conversion in PV systems. However, it is difficult for nontechnical solar energy consumers to define different parameters of the controller and deal with distinct sources of data related to the planning. Semantic Web technologies enable us to improve knowledge representation, sharing, and reusing of relevant information generated by various sources. In this work, we propose a knowledge-based model representing key concepts associated with an MPPT-based controller. The model is featured with Semantic Web Rule Language (SWRL), allowing the system planner to extract information about power reductions caused by snow and several airborne particles. The proposed ontology, named MPPT-On, is validated through a case study designed by the System Advisor Model (SAM). It acts as a decision support system and facilitate the process of planning PV projects for non-technical practitioners. Moreover, the presented rule-based system can be reused and shared among the solar energy community to adjust the power estimations reported by PV planning tools especially for snowy months and polluted environments