A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electricity demand as well as its dependency on various external factors renders STLF a challenging task. To that end, several deep learning models have been proposed in the literature for STLF, reporting promising results. In order to evaluate the accuracy of said models in day-ahead forecasting settings, in this paper we focus on the national net aggregated STLF of Portugal and conduct a comparative study considering a set of indicative, well-established deep autoregressive models, namely multi-layer perceptrons (MLP), long short-term memory networks (LSTM), neural basis expansion coefficient analysis (N-BEATS), temporal convolutional networks (TCN), and temporal fusion transformers (TFT). Moreover, we identify factors that significantly affect the demand and investigate their impact on the accuracy of each model. Our results suggest that N-BEATS consistently outperforms the rest of the examined models. MLP follows, providing further evidence towards the use of feed-forward networks over relatively more sophisticated architectures. Finally, certain calendar and weather features like the hour of the day and the temperature are identified as key accuracy drivers, providing insights regarding the forecasting approach that should be used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble, N-BEATS, Temporal Convolution, Forecasting Accurac

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Investigating the impact of asset condition on distribution network reconfiguration and its capacity value

Ph. D. ThesisGenerally, decisions regarding Distribution Network (DN) operations are based only on operational parameters, such as voltages, currents and power flows. Asset condition is a key parameter that is usually not considered by Network Management Systems (NMSs) in their optimization process. The work in this thesis seeks to quantify the extent to which asset condition information can positively influence network operation and planning; specifically through Distribution Network Reconfiguration (DNR). Asset condition can be translated into Health Indices (HIs) and failure rates, allowing an NMS – or an optimization algorithm – to make better informed decisions. This is realized via appropriate asset condition assessment and failure rate models. The effect on optimal DNR is evaluated – focusing on substation condition and reliability; the idea of load transfer from one feeder or substation to a more reliable one is key in the proposed methodology. Condition-based risk is considered in the DNR problem, and the impact of transformer ageing on network reconfiguration is examined as well. The effect of asset condition assessment and ageing – which depends on the type of network branches (overhead lines or underground cables) – on the optimal distribution switch automation is also investigated. Finally, a probabilistic method is developed to quantify the contribution of DNR to network security considering asset condition and ageing. The results show that savings can be in the order of tens of thousands of U.S. dollars for a single DN; this corresponds approximately to 10% of the annual cost of active power losses. This can mean hundreds of thousands – or even millions – of U.S. dollars of savings for a single DN operator. Regarding the optimal placement of automated switches, neglecting the effect of asset ageing can result in an underestimation of expected outage cost by as much as $223,000 over a 20-year period. Finally, ignoring the contribution of DNR to security of supply can double the estimation of network risk; in addition to that, disregarding asset condition and ageing results in a reinforcement deferral overestimation of two years

Power Electronics Applications in Renewable Energy Systems

The renewable generation system is currently experiencing rapid growth in various power grids. The stability and dynamic response issues of power grids are receiving attention due to the increase in power electronics-based renewable energy. The main focus of this Special Issue is to provide solutions for power system planning and operation. Power electronics-based devices can offer new ancillary services to several industrial sectors. In order to fully include the capability of power conversion systems in the network integration of renewable generators, several studies should be carried out, including detailed studies of switching circuits, and comprehensive operating strategies for numerous devices, consisting of large-scale renewable generation clusters

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems