• Energy Research

The adoption of residential-scale low carbon technologies, such as photovoltaic panels or electric vehicles, is expected to significantly increase in the near future. Therefore, it is important for distribution network operators (DNOs) to understand the impacts that these technologies may have, particularly, on low voltage (LV) networks. The challenge, however, is that these LV networks are large in number and diverse in characteristics. In this work, four clustering algorithms (hierarchical clustering, $k$ - ${\rm medoids}$ ++, improved $k$ - ${\rm means}$ ++, and Gaussian Mixture Model—GMM), are applied to a set of 232 residential LV feeders from the North West of England to obtain representative feeders. Moreover, time-series monitoring data, presence of residential-scale generation, and detailed customer classification are considered in the analysis. Multiple validity indices are used to identify the most suitable algorithm. The improved $k$ - ${\rm means}$ ++ and GMM showed the best performances resulting in eleven representative feeders with prominent characteristics such as number and type of customers, total cable length, neutral current, and presence of generation. Crucially, the results from studies performed on these feeders can then be extrapolated to those they represent, simplifying the analyses to be carried out by DNOs. This is demonstrated with a hosting capacity assessment of photovoltaic panels in LV feeders.

The definition of the bidding zones is an essential aspect to develop an electricity market on large power systems, by trading off between situations with uniform price and nodal prices. In this paper, the results of the application of a set of clustering algorithms (k-means, k-medoids, hierarchical clustering, and price differential clustering) to the formation of the bidding zones are presented. The classical versions of the methods used require post-processing to identify connected bidding zones. The customised versions of the methods incorporate topology constraints in the clustering procedures. The input data are given by locational marginal prices (LMPs), power transfer distribution factors (PTDFs), and network topology information. The clustering algorithms have been applied to the study of a reduced model of the European transmission system, for different numbers of clusters. The results have been assessed by using two synthetic indicators that represent clustering validity and the possible occurrence of market power in the bidding zones formed. Finally, a conjecture has been expressed: methods that use LMPs tend to be more effective according with classical clustering validity-based indicators, while methods that use PTDFs tend to be more effective according with market power-based indicators.

"This paper exploits the Duration-of-Use of the demand patterns as a key concept for dealing with demand side flexibility. Starting from the consideration that fine-grained energy metering is not used at the point of supply of the electricity consumers, i.e., the granularity of the energy measured (at time steps of 15 minutes, 30 minutes or one hour) is not sufficient to represent the variability of the demand patterns, event-based energy metering (EDM) is indicated as a viable option to provide a very detailed reconstruction of the demand patterns. In particular, the use of EDM enables high-quality tracking of the demand peaks with a reduced number of data with respect to the ones needed to measure energy at regular time steps for reaching a similar peak tracking capability. From the EDM outcomes, a new class of options for setting up tariffs or contracts for flexibility, based on the demand duration curve, is envisioned. "

The allocation of the system losses to suppliers and consumers is a challenging issue for the restructured electricity business. Meaningful loss allocation techniques have to be adopted to set up appropriate economic penalties or rewards. The allocation factors should depend on size, location, and time evolution of the resources connected to the system. In the presence of distributed generation, the variety of the power flows in distribution systems calls for adopting mechanisms able to discriminate among the contributions that increase or reduce the total losses. Some loss allocation techniques already developed in the literature have shown consistent behavior. However, their application requires computing a set of additional quantities with respect to those provided by the distribution system power flow solved with the backward/forward sweep approach. This paper presents a new circuit-based loss allocation technique, based on the decomposition of the branch currents, specifically developed for radial distribution systems with distributed generation. The proposed technique is simple and effective and is only based on the information provided by the network data and by the power flow solution. Examples of application are shown to confirm its effectiveness

The demand side in a power system has key importance in the evolving context of the energy systems. Exploitation of possible flexibilities of the customer's behavior is considered as an important option to promote demand response programmes and to achieve greater energy savings. For this purpose, the first action required is to augment availability of information about consumption patterns. The electricity consumption in a residential system is highly dependent on various types of uncertainties due to the diverse lifestyle of customers. Knowledge about the aggregated behavior of residential customers is very important for the system operator or aggregator to manage load and supply side flexibilities for economic operation of the system. In this paper, the effect of sampling time is evaluated for different residential load aggregations using probabilistic approach. A binomial probability distribution model is used to extract trends in increase or decrease in demand with respect to time evolution of a typical day. For each case study scenario, confidence intervals are calculated to assess the uncertainty and randomness in load variation trends. The findings of this study will lead towards better management of demand and supply side resources in a smart grid and especially for microgrids.

28 June 2018 was the 100th anniversary of the first presentation on symmetrical components made by Charles LeGeyt Fortescue at the 34th Annual Convention of the American Institute of the Electrical Engineers in Atlantic City (NJ, USA). The introduction of the symmetrical component concept was immediately seen as a milestone for electrical system studies, and many applications have been developed during the ensuing years. Today, refined or advanced contributions to conceptual and practical aspects of electrical applications are still being proposed based on the powerful structure of symmetrical components. This paper recalls the most significant steps made in the electrical engineering field after the introduction of the symmetrical component theory, and indicates recent developments concerning the studies on electrical machines, harmonics and interharmonics in different applications, and the operation of power and distribution systems with distributed energy resources.