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Notwithstanding the rapidly growing body of literature on interdisciplinary research, several of the crucial dynamics in interdisciplinary research systems remain poorly understood and undertheorized. To this effect we seek to provide a 'fundamental' ontology of interdisciplinary research systems. We principally understand an interdisciplinary research system as a complex system consisting of researchers from different disciplines that have undergone a pseudomorphosis (i.e. a false formation) into a complicated system through the formation of a central organizing principle. The central organizing principle provides a stricter definition of the research problem and subsequently coalesces the intentionality of system agents through a unification of their disparate aims and methodologies. This pseudomorphosis is thereby associated with an exchange of individual freedom for organizational utility resulting in internal tensions which are, we argue, most prominently expressed in the interplay of epistemic incompatibilities between disciplines. We explore three frameworks for successfully navigating these incompatibilities: circumvention, which is based on avoidance of areas of disciplinary incompatibility; pragmatism, which is based on ignoring areas of compatibility; and disciplinary synthesis which involves a paradigm shift in researchers' understanding of their disciplinary propositions resolving perceived incompatibilities. It is anticipated that this paper may be of benefit to researchers and organizers seeking to effectively structure interdisciplinary research projects, specifically in terms of framing the research problem and the modes of inquiry, and in structuring the interdisciplinary research team. [ABSTRACT FROM AUTHOR]

Collaborative research has become increasingly prominent since the mid-20th century. This article aspires to offer a fundamental ontology of a multidisciplinary research system. As a point of departure, we consider disciplinarity as a restricted language code as noted by Bernstein. The impetus for collaboration is found in a research problem's transcendence of disciplinary bounds. This article makes several propositions that diverge from the consensus position regarding the formation and dynamics of a multidisciplinary system. Most notably that such a system adheres to the constituent elements of what could be regarded as a complex system, including an ensemble of elements, interactions between these elements, local disorder followed by the emergence of robust order and system memory. We propose that the internal communications and subsequent self-organization of such a system may be conceptualized as orientation signals, or 'stigmergy', analogous to those observed in swarms. System robustness, we argue, is a function of the individual researcher's local autonomy and is, paradoxically, augmented by the weakness of communications across disciplinary bounds, along with the lack of central organization and the emphasis on research novelty. System memory, we argue, manifests itself in the ability of a researcher to change her/his route of inquiry, based on environmental feedback, whereby new information becomes incorporated into the adjusted research methodology. We propose that an emergent intelligence, at the level of the system, expresses itself in the unconcealment of the 'form' of the metaproblem. The theoretical model is empirically illustrated using, as an example, the contemporary field of renewable energy research, which is an area primed for collaborative research. It is anticipated that an improved understanding of multidisciplinary research systems provides insights into certain strengths particular to less integrated and self-organized forms of collaborative research along with a framework with which to improve the design and fostering of such systems. [ABSTRACT FROM AUTHOR]

Wind is a naturally variable resource that fluctuates across timescales and, by the same token, the electricity generated by wind also fluctuates across timescales. At longer timescales, i.e., hours to days, synoptic-scale weather systems, notably cold fronts during South African winter months, are important instigators of strong wind conditions and variability in the wind resource. The variability of wind power production from aggregates of geographically disperse turbines for the passage of individual cold fronts over South Africa was simulated in this study. When considering wind power variability caused by synoptic-scale weather patterns, specifically cold fronts, the timescale at which analysis is conducted was found to be of great importance, as relatively small mean absolute power ramps at a ten-minute temporal resolution, order of 2-4% of simulated capacity, can result in large variations of total wind power production (at the order of 32–93% of simulated capacity) over a period of three to four days as a cold front passes. It was found that when the aggregate consists of a larger and more geographically dispersed set of turbines, as opposed to a smaller set of turbines specifically located within cold-front dominated high wind areas, variability and the mean absolute ramp rates decrease (or gets ‘smoothed’) across the timescales considered. It was finally shown that the majority of large simulated wind power ramp events observed during the winter months, especially at longer timescales, are caused by the passage of cold fronts. [ABSTRACT FROM AUTHOR]

• Exogenous atmospheric variables may be used to improve wind speed forecasts. • Variable predictor skill is tested as a function of domain size, period and elevation. • u- and v model wind speed components, vertical velocity, atmospheric divergence, and pressure levels best improved wind speed prediction. The use of exogenous meteorological variables in wind speed prediction is currently enjoying much attention, particularly with the continued proliferation of wind power onto electricity networks. Typically, the selection of appropriate forecasting regimes or downscaling variables, along with the appropriate domain size, is based on expert knowledge and/or physical principles. There is however little consensus as to which variables are the best predictors. In this paper the wind speed predictive skill provided by 16 exogenous meteorological variables in machine learning algorithms is investigated as a function of: choice of regression model; choice of domain size; forecast period; meteorological variable elevation (i.e. pressure level); and testing site location. The principal components of numeric reanalysis datasets for each of 16 variables, along with lagged winds speeds, were employed as predictors at each of the test sites. The mean best performing wind speed predictors were found to be 950 hPa- vertical velocity; divergence; the u- & v-wind speed components; and geopotential heights. The ability to investigate and rank the forecast skill provided by meteorological predictors is important in gaining insight into physical processes influencing the wind resource and the improvement of wind speed forecasts. Though distinct predictors, domains and approaches were identified to significantly improve forecasting accuracy for a specific site, generalisations were found to be difficult to make, even in the small geographic area investigated. Therefore, it is recommended is that process described in paper is repeated for individual sites in selecting exogenous predictor variables. [ABSTRACT FROM AUTHOR]

• Novel methodology to assess the causative factors of wind power ramps developed. • Probabilistic relationship between ramp events and atmospheric states established. • Appropriate spectral parameters selected for stochastic wind power simulation. • Thermal land-sea breeze interaction identified as a primary initiator of ramps. The relationship between wind power ramp events and their causative weather systems remains poorly understood, despite its importance to the development of ramp forecasting procedures. Results from previous studies linking ramp events and weather systems have proven difficult to generalize and methodologies used may be difficult to duplicate, especially in cases of measured data scarcity. Accordingly, this paper proposes a flexible methodology for investigating this link between ramps and weather systems in instances of measured data scarcity. A historic wind power time-series is firstly simulated by applying stochastic variations to numeric weather prediction (NWP) reanalysis data. Ramps events are identified within the time-series using a swinging door algorithm. Temporal regularities in ramp statistics are identified as these provide probabilistic insights into ramp occurrences. Finally, ramps are linked to a set of atmospheric circulation archetypes. These archetypes are identified by applying self-organizing maps as a classification procedure to historic NWP data. The proposed methodology is demonstrated through a case study considering a wind farm in South Africa. It is found that mean power and power variability differ significantly as a function of atmospheric circulation, and that thermally driven land-sea breeze interaction can be a primary mechanism for ramp events. [ABSTRACT FROM AUTHOR]

According to South Africa's national energy policy, network penetration of variable renewable energy (VRE) generation will significantly increase by 2030. Increased associated network uncertainty creates the need for an additional flexible generation. As the planned VRE is mostly non-synchronous PV and wind generators, additional ancillary services will also be required. Pumped Storage (PS), which is a well-established flexible generation technology with fast ramping capability and the ability to contribute various ancillary services, could help integrate increased VRE penetration on the South African network. However, in the latest revision of South Africa's energy policy, PS was left out in favor of gas turbines and batteries as favored flexible generation options. This paper explores the two-part hypothesis that PS was disadvantaged in the formulation of a national energy mix due to: (a) ancillary services provided by PS not being explicitly monetized in energy modeling software; (b) the uncertainties associated with project costing assumptions. The value of PS in terms of providing ancillary services is firstly explored using the international literature. Secondly, the impact of input-cost uncertainties is demonstrated by comparing pumped storage, gas turbines, and batteries using levelized cost of energy (LCOE) curves and the Tools for Energy Model Optimization and Analysis (Temoa), North Carolina State University, USA, optimization software. Based on LCOE calculations using revised cost assumptions, it is found that PS may indeed be preferential to gas turbines or batteries, particularly at large load factors. The authors hope that this research contributes to the scientific understanding of the role that PS can play in supporting the integration of generation from renewable sources for effective grid operations. [ABSTRACT FROM AUTHOR]