A Study of the Effects of Time Aggregation and Overlapping within the Framework of IEC Standards for the Measurement of Harmonics and Interharmonics

The increasing incorporation of power electronics and other non-linear loads, in addition to their energy advantages, also implies a poor power quality, especially as regards harmonic pollution. Different solutions have been proposed to measure harmonic content, taking the International Electrotechnical Commission (IEC) standards as a reference. However, there are still some issues related to the measurement of the harmonic, and especially, interharmonic content. Some of those questions are addressed in this work, such as the problem derived from the instability of the values obtained by applying the discrete Fourier transform to each sampling window, or the appearance of local peaks when there are tones separated by multiples of the resolution. Solutions were proposed based on time aggregation and the overlapping of windows. The results demonstrate that aggregation time, window type, and overlapping can improve the accuracy in harmonic measurement using Fourier transform-based methods, as defined in the standards. The paper shows the need to consider spectral and time groupings together, improving results by using an appropriate percentage of overlap and an adaptation of the aggregation time to the harmonic content

Methodology for Energy Management in a Smart Microgrid Based on the Efficiency of Dispatchable Renewable Generation Sources and Distributed Storage Systems

This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energías Renovables—Centro de Investi-gaciones Energéticas, Medioambientales y Tecnológicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 kWh per year, which is equal to savings of more than 8500 €. It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting

Conversion of a Network Section with Loads, Storage Systems and Renewable Generation Sources into a Smart Microgrid

This paper shows an experimental application case to convert a part of the grid formed by renewable generation sources, storage systems, and loads into a smart microgrid. This transformation will achieve greater efficiency and autonomy in its management. If we add to this the analysis of all the data that has been recorded and the correct management of the energy produced and stored, we can achieve a reduction in the electricity consumption of the distribution grid and, with this, a reduction in the associated bill. To achieve this transformation in the grid, we must provide it with intelligence. To achieve this, a four steps procedure are proposed: identification and description of the elements, integration of the elements in the same data network, establishing communication between the elements and the control system, creating an interface that allows control of the entire network. The microgrid of CEDER-CIEMAT (Renewable Energy Centre in Soria, Spain) is presented as a real case study. This centre is made up of various sources of generation, storage, and consumption. All the elements that make up the microgrid are incorporated into free software, Home Assistant, allowing real-time control and monitoring of all of them thanks to the intelligence that has been provided to the grid. The novelty of this paper is that it describes a procedure that is not reported in the current literature and that, being developed with Home Assistant, is free and allows the control and management of a microgrid from any device (mobile, PC) and from any place, even though not on the same data network as the microgrid

Power Consumption Analysis of Electrical Installations at Healthcare Facility

This paper presents a methodology for power consumption estimation considering harmonic and interharmonic content and then it is compared to the power consumption estimation commonly done by commercial equipment based on the fundamental frequency, and how they can underestimate the power consumption considering power quality disturbances (PQD). For this purpose, data of electrical activity at the electrical distribution boards in a healthcare facility is acquired for a long time period with proprietary equipment. An analysis in the acquired current and voltage signals is done, in order to compare the power consumption centered in the fundamental frequency with the generalized definition of power consumption. The results obtained from the comparison in the power consumption estimation show differences between 4% and 10% of underestimated power consumption. Thus, it is demonstrated that the presence of harmonic and interharmonic content provokes a significant underestimation of power consumption using only the power consumption centered at the fundamental frequency

Spectral Kurtosis Based Methodology for the Identification of Stationary Load Signatures in Electrical Signals from a Sustainable Building

The increasing use of nonlinear loads in the power grid introduces some unwanted effects, such as harmonic and interharmonic contamination. Since the existence of spectral contamination causes waveform distortion that may be harmful to the loads that are connected to the grid, it is important to identify the frequency components that are related to specific loads in order to determine how relevant their contribution is to the waveform distortion levels. Due to the diversity of frequency components that are merged in an electrical signal, it is a challenging task to discriminate the relevant frequencies from those that are not. Therefore, it is necessary to develop techniques that allow performing this selection in an efficient way. This paper proposes the use of spectral kurtosis for the identification of stationary frequency components in electrical signals along the day in a sustainable building. Then, the behavior of the identified frequencies is analyzed to determine which of the loads connected to the grid are introducing them. Experimentation is performed in a sustainable building where, besides the loads associated with the normal operation of the building, there are several power electronics equipment that is used for the electric generation process from renewable sources. Results prove that using the proposed methodology it is possible to detect the behavior of specific loads, such as office equipment and air conditioning

Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing