• Energy Research

Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis.

Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more complex, requiring specific technologies to balance it. In this sense, the demand-side management is one of its techniques; the demand response is a promising approach for providing Flexibility Services (FSs) and complying with the regulatory directives of the energy market. As a solution, this paper proposes the use of the OpenADR (Open Automated Demand Response) standard protocol in combination with a Decentralized Permissioned Market Place (DPMP) based on Blockchain. On one hand, OpenADR hierarchical architecture based on distributed nodes provides communication between stakeholders, adding monitoring and management services. Further, this architecture is compatible with an aggregator schema that guarantees the compliance with the strictest regulatory framework (i.e., European market). On the other hand, DPMP is included at different levels of this architecture, providing a global solution to Flexibility Service Providers (FSP) that can be adapted depending on the regulation of a specific country. As a proof of concept, this paper shows the result of a real experimental case, which implements a Capacity Bidding Program where the OpenADR protocol is used as a communication method to control and monitor energy consumption. In parallel, the proposed DPMP based on Blockchain makes it possible to manage the incentives of FSs, enabling the integration of local and global markets.

Microgrids (MGs) have emerged as a potential solution for the integration of Distributed Energy Resources (DERs) into the distribution network. In this sense, to effectively manage MGs, it is essential to implement Energy Management Systems (EMSs). This entails not only performing the unit commitment but also considering the voltage and reactive power technical constraints and managing ancillary services. This paper contributes with a comprehensive EMS for the optimal management of active and reactive power of a generic grid-tied MG composed of Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Diesel Generator (DGs) units and loads, with the goal of reducing the operating costs of the facility. The EMS includes models for the power electronics units to apply reactive power management and a generic formulation for the management of the startup and shutdown cycles of dispatchable units. Furthermore, a detailed modeling of BESS and DG units is presented, reflecting the actual behavior of the devices. The MG is modeled as a multi-busbar network, with the application of the power flow equations to establish the link between power flows and nodal voltages. All the constraints are linearized to formulate the EMS as a Mixed-Integer Linear Programming (MILP) optimization problem. The EMS is validated in a real facility: the CATEPS Microgrid Living-Lab. The results demonstrate the operational effectiveness of the EMS in different seasons, exhibiting a reduction in costs ranging from 21.84 % in summer to a 5.69 % in winter compared to a scenario with RES production but without energy management. In addition, a comprehensive examination of reactive power and voltage management is presented. Furthermore, an empirical assessment of the power flow equations linearization demonstrated minimal discrepancy in the results when compared with those obtained with the non-linear equations, exhibiting a mean absolute error of 8.8e-5 p.u. and 3.2e-5 rad in voltage magnitude and phase angle, respectively, in the most unfavorable scenario. A sensitivity analysis of the startup and shutdown cycles management of the BESS reveals a negligible effect on operational costs, yet it provides a mechanism for managing the battery stress by reducing the number of startups in a complete week from 28 to 16 in summer and from 37 to 24 in winter. The dependence between the maximum charging and discharging power on the state of charge of the BESS is also assessed in the use case.

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

Knowledge of customer phase connection in low-voltage distribution networks is important for Distribution System Operators (DSOs). This paper presents a novel data-driven phase identification method based on Bayesian inference, which uses load consumption profiles as inputs. This method uses a non-linear function to establish the probability of a customer being connected to a given phase, based on variations in the customer’s consumption and those in the phase feeders. Owing to the Bayesian inference, the proposed method can provide up-to-date certainty about the phase connection of each customer. To improve the detection of those customers that are more difficult to identify, after obtaining the up-to-date certainty for all users, the consumption of those who have an up-to-date certainty above a certain percentile compared with the rest of the substation (those that are more likely to be correctly classified) is subtracted from the phase in which they are classified. The performance of the proposed method was evaluated using a real (non-synthetic) low-voltage distribution network. Favourable results (with accuracies higher than 97 %) were obtained in almost all cases, regardless of the percentage of Smart Meter penetration and the size of the substation. A comparison with other state-of-the-art methods showed that the proposed method outperforms (or equals) them. The proposed method does not necessarily require previously labelled data; however, it can handle them even if they contain errors. Having previous information (partial or complete) increases the performance of phase identification, making it possible to correct erroneous previous labelling.

This paper analyzes a case study on electrical power control and energy management of a 60 apartments’ residential building with solar generation and energy storage in Santiago, Chile. This constitutes both, a challenge and an opportunity, which have not yet been fully addressed by the Chilean regulatory framework, under the perspective of renewable energy sources’ integration with the local electric utility, ENEL. Under this scenario, a set of strategies for the coordination and control of the electricity supply versus demand is tested and adapted for the specific needs of the customers and the infrastructure of the network. The microgrid operates in full coordination with the grid to maximize green energy supply vs demand and systems capacity, whereby the different energy consumers and their consumption profiles play a crucial role as “active loads”, as they are able to respond and adapt to the needs of the grid-tie microgrid while enjoying economic benefits. Simulations results are presented under different tariff options, systems capacity and energy storage alternatives, so as to compare the proposed strategies with the actual case. Results show the advantage of the proposed electric tariffs and energy management strategies for the integration of distributed generation systems in the context of the smart grid transformation by ENEL in Chile.

In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCADTM X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them.