• Energy Research

This paper proposes a method for evaluating the optimal configuration of a hybrid system (biomass power plant and photovoltaic plant), which is connected to the electrical grid, to achieve minimum energy costs. The study is applied to a small rural municipality in the Valencian Community, Spain, as an energy community. The approach takes into account the daily energy demand variation and price curves for energy that are either imported or exported to the grid. The optimal configuration is determined by the highest internal rate of return (IRR) over a 12-year period while providing a 20% discount in electricity prices for the energy community. The approach is extrapolated to an annual period using the statistical data of sunny and cloudy days, considering 23.8% of the year as cloudy. The methodology provides a general procedure for hybridising both plants and the grid to meet the energy needs of a small rural population. In the analysed case, an optimal combination of 140 kW of rated power from the biogas generator was found, which is lower than the maximum demand of 366 kW and 80 kW installed power in the photovoltaic plant, resulting in an IRR of 6.13% over 12 years. Sensitivity studies for data variations are also provided.

[EN] The significant and continuous increment in the global electricity consumption is asking for energy saving strategies. Efficient control for heating, ventilation and air-conditioning systems (HVAC) is the most cost-effective way to minimize the use of energy in buildings. In this framework, an energy management and control system (EMCS) has been developed to schedule electricity end-uses in the campus of the Universidad Politecnica de Valencia (UPV), Spain. This paper presents an evaluation performed by using the EMCS of different control strategies for HVAC split systems. It analyzed the effect of different schedules for a common air-conditioning device and demand response strategies are tested in several situations. The economic saving is calculated taking into account the electricity contract clauses. Finally, a test is made for the control of a group of similar devices in order to reduce the maximum peak power in consumption and to obtain a flexible load shape with the HVAC loads. The studies are then extrapolated to a larger system, the whole University campus, for which energy and economic savings are quantified. (C) 2010 Published by Elsevier B.V. This research work has been possible with the support of Universidad Politecnica de Valencia (Spain) by the grant #CE 19990032.

[EN] The monitoring of industry processes can optimize the use of resources and improve its efficiency. In dairy farms, several parameters from the processes must be monitored. This paper proposes the design of a monitoring system for a farm that produces dairy products and jams. Several similar studies are analysed. Based on this, and on the farm characteristics, a novel design is developed. The principal benefits of the system are also exposed. This work was supported by Universidad de las Americas - Ecuador under project SIS.JCG.19.01. The authors would like to thank Monica Guevara, Andrea Maldonado, and Francisca Martinez from Universidad de las Americas - Ecuador for their valuable help and information.

Numerous computer programs are available to calculate the energy efficiency of buildings. Many are based on the use of a reference building to determine the degree of efficiency. However, this methodology presents a major constraint when assessing a building after construction and during the use stage. To assess the energy efficiency of buildings, this paper proposes new indices based on energy consumption during different periods; as well as other parameters of the buildings such as construction area, number of users, and air-conditioned volume. Results are presented as an application for various buildings on the campus of the Universidad Politecnica de Valencia.

Demand response has proven to be a useful mechanism that produces important benefits for both the customer and the power system. In the context of an increasingly competitive electricity market, where prices are constantly rising and the presence of renewable energy resources is gaining prominence, this paper analyzes the flexibility potential of customers in the meat industry, based on the management of the most energy consuming process in this type of segment: cooling production and distribution. The effectiveness of the proposed actions has been successfully tested and validated in an active factory that produces cured ham in Spain, where savings of about 5% in the total annual cost of electricity have been assessed, together with power reductions in the range of 50% of the total peak demand of the studied facilities. Such results demonstrate the efficacy of these techniques, and they open the door to an innovative perspective on the evaluation of flexibility among customers which are traditionally considered rigid, providing a novel approach to the management of customer infrastructures in order to exploit their flexibility in electricity markets. This work was supported by the Spanish Government (Ministerio Ciencia e Innovacion, MICINN) under Research Project ENE2007-67771-C02-01&02/CON.

[EN] The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).

Engineers often conduct energy audits in buildings in use with the aim of improving the energy consumption of the facilities. The problem with this traditional approach is that the audits take place at a given instant in time. Subsequently, it is necessary to track the amount of energy continuously consumed in buildings in order to deal with an energy efficiency problem. This paper proposes the continuous assessment of energy efficiency in buildings using different energy rating factors. These factors have already been presented in other works by the authors, and the usefulness of these factors for assessing the energy performance of buildings and identifying wasted energy has been proven. This article presents the results of applying the methodology in 55 buildings located on the Universitat Politècnica de València campus during an entire year. This research work has been possible with the support of the Universitat Politecnica de Valencia through grant CE19990032.

[EN] Demand prediction has been playing an increasingly important role for electricity management, and is fundamental to the corresponding decision-making. Due to the high variability of the increasing electrical load, and of the new renewable energy technologies, power systems are facing technical challenges. Thus, short-term forecasting has crucial utility for generating dispatching commands, managing the spot market, and detecting anomalies. The techniques associated with machine learning are those currently preferred by researchers for making predictions. However, there are concerns regarding limiting the uncertainty of the obtained results. In this work, a statistical methodology with a simple implementation is presented for obtaining a prediction interval with a time horizon of seven days (15-min time steps), thereby limiting the uncertainty. The methodology is based on pattern recognition and inferential statistics. The predictions made differ from those from a classical approach which predicts point values by trying to minimize the error. In this study, 96 intervals of absorbed active power are predicted for each day, one for every 15 min, along with a previously defined probability associated with the real values being within each obtained interval. To validate the effectiveness of the predictions, the results are compared with those from techniques with the best recent results, such as artificial neural network (ANN) long short-term memory (LSTM) models. A case study in Ecuador is analyzed, resulting in a prediction interval coverage probability (PICP) of 81.1% and prediction interval normalized average width (PINAW) of 10.13%, with a confidence interval of 80%.