Sustainability has become increasingly important to research and practice. In order to determine impacts, identify improvement potential and to disclose efforts towards sustainability, an organization needs appropriate reporting. Thus, sustainability reporting has become a topic of broader interest, for example, to assess own situations, enable benchmarking, communicate own efforts and improve trust. Although sustainability reporting is a complex issue, only limited research and guidelines for higher education institutions (HEI) are available. Accordingly, negative impacts occur such as regarding the standardization and, thus, the comparability of reports. This article describes and demonstrates how different approaches from research related to reporting (information systems research in particular) and sustainability can be transferred to the field of sustainability reporting in HEIs to leverage the applicability of such reports. As a result, classifications of existing indicators and methodical approaches are provided, which are based on the analysis of a campus management system and different reporting standards as well as reporting knowledge in general. These classifications indicate that financial aspects are often focused and environmental issues are neglected. Moreover, the findings emphasize the importance of further multidimensional research on different topics such as (re-)development of specific indicators for HEIs, (re-)design of campus management systems and extension of current reporting standards. Therfore, a research agenda-with 18 agenda items-that synthesizes the presented directions is proposed. This agenda can be used to position further research or to derive new and innovative research questions.
Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.
Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users' cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the bestperforming DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework's applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.
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