Browsing by Author "Van Staden, Chantelle"

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    Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support
    (Stellenbosch : Stellenbosch University, 2022-04) Van Staden, Chantelle; Vermeulen, Johan; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: South Africa has displayed a unique energy supply profile over recent years, where the ability to consistently meet the energy demand has been constrained by physical limitations of the current energy supply infrastructure. The inadequate supply infrastructure results in countrywide loadshedding events, where total energy supply within high demand periods cannot be met. Low-grade coal, poorly maintained power plants and the impending decommissioning of existing thermal plants adds to the country’s energy supply deficit. Inadequate supply in high demand periods typically requires response from expensive on demand dispatch units, which are often non-renewable resources. This also equates to a decrease in grid supply stability. It is expected that optimised geospatial capacity allocation of new build wind and solar plants can assist in addressing the generation capacity constraints in the medium to longer term future. The framework proposed in this study favours a cascaded optimisation strategy, whereby the residual load profile is optimised statistically to reduce the requirements of ancillary services to complement baseload generation. In support of a reliable future energy supply scenario with high penetration of renewable energy, the optimisation framework proposed in this work represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, renewable energy resource features are categorised in terms of the statistical properties of the spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time-of-Use periods. In this context, it is recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time-of-Use periods are more important than others. Apart from the benefit of assigning renewable energy capacities to spatial regions rather than specific coordinates, clustering reduces the dimensions of input data sets dramatically. This reduces the dimensionality of the multi-variable optimisation search space, which translates to reduced risk of local minima and reduced computational cost. The proposed framework has been implemented for a number of baseline case studies and optimisation case studies. It is concluded that the framework is highly flexible in the sense that the formulation of the minimum and maximum allocation constraints allow application for real-world scenarios where capacity allocation constraints apply on a regional level. Overall, the optimisation framework provides a robust method for the geospatial capacity allocation of wind and solar resources. The framework employs a robust way of handling constraint scenarios when considering multiple highly granular resource clusters.