Network capacity charge for sustainability and energy equity: A model-based analysis

© 2020 Elsevier Ltd It is long known that the afternoon peak demand accounts for over-investment in the electricity network assets. This results in a high price of delivered electricity which does not fairly differentiate between peak and non-peak users. Energy tariff is proven to be one of the best demand-side management (DSM) tools for shaping consumers’ behaviour. While electricity pricing models, such as inclining block and time-of-use tariffs, have received decent attention as successful mechanisms, there are little discussions about another efficient tariff known as a rollover network capacity charge. It is a penalty for the highest recorded power usage over the previous reading cycle (or year) which is introduced to commercial users in some jurisdictions. With recent price reduction in distributed generation and storage (DGS) systems, the interest has increased in devising policies for directing the household and commercial consumers’ behaviour towards using DGS systems in line with DSM objectives. In this paper, we have integrated the rollover network capacity charge into DGS systems investment analysis. The introduced optimisation formulation can consider capacity charge for both energy import and export. The results from a few case studies show the positive impact of capacity charge in directing the peak-consumers’ investment decisions towards DSM tools (e.g., energy storage) to curb their peak demands. This not only improves the resilience of the network but also promises as an effective mechanism in energy-justice nexus by avoiding the transfer of the associated costs of peak demand to all users

A decision support tool for multi-attribute evaluation of demand-side commercial battery storage products

Publisher Copyright: © 2021 Elsevier LtdWith the diversification of commercial energy storage technologies, choosing a suitable technology is becoming a complex decision-making process. The complexity is rooted in the many decision criteria such as technology, brand reputation, energy capacity, volume, weight, aging, and warranty among many others. As such, for non-expert users, particularly small households or enterprises, the act of energy storage adoption is becoming growingly cumbersome. To address this problem, this paper introduces a decision support tool for the evaluation of commercial (small-scale) energy storage products. It then identifies the most suitable option(s) based on the users' preferences. For the reasons elaborated in the paper, nine multi-criteria decision-making (MCDM) methodologies have been employed. Altogether, 19 attributes are identified for the evaluation of (battery) energy storage technologies. The decision support tool is developed in the Matlab environment and includes a graphical user interface for easier interaction of non-expert users. For the demonstration, three scenario cases have been studied for users with different preferences. The ranking results clearly show the marked impact of users preferences on the recommended energy storage technologies. This implies that a tool like this can help small users in the selection of their right technology and avoid resource loss due to inappropriate technology selection, which can be neither economical nor sustainable.Peer reviewe

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

Literature is rich in methodologies for “aggregated” load forecasting which has helped electricity network operators and retailers in optimal planning and scheduling. The recent increase in the uptake of distributed generation and storage systems has generated new demand for “disaggregated” load forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power. In the setting studied here, it was shown that forecast errors can be reduced by using a coarser forecast granularity. It was also found that one year of historical data is sufficient to develop a load forecast model for residential customers as a further increase in training dataset has a marginal benefit

Network of networks: A bibliometric analysis

This study explores the evolving structure of the rising field of “network of networks” (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., “interconnected networks”, “multilayer networks”, “multiplex networks”, “interdependent networks”, “multinetworks”, “multilevel networks”, and “multidimensional networks”). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as “interconnection”, “multilayer”, and “interdependence”, gradually spread to computer and physical sciences. As of 2018, network interdependence – with its application in network resilience and prevention of cascading failure – seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels