Eva Gonzalez-Romera

Monthly Electric Energy Demand Forecasting Based on Trend Extraction

Medium-term electric energy demand forecasting is an essential tool for power system planning and operation, mainly in those countries whose power systems operate in a deregulated environment. This paper proposes a novel approach to monthly electric energy demand time series forecasting, in which it is split into two new series: the trend and the fluctuation around it. Then two neural networks are trained to forecast them separately. These predictions are added up to obtain an overall forecasting. Several methods have been tested to find out which of them provides the best performance in the trend extraction. The proposed technique has been applied to the Spanish peninsular monthly electric consumption. The results obtained are better than those reached when only one neural network was used to forecast the original consumption series and also than those obtained with the ARIMA method

Power Injection System for Grid-Connected Photovoltaic Generation Systems Based on Two Collaborative Voltage Source Inverters

This paper presents a new topology for the power injection system that is based on the parallel association of two voltage source inverters. One operates using a quasi-square voltage waveform strategy, and the other operates with a pulsewidth-modulation (PWM)-based strategy. The aims of this topology are to inject the power from the photovoltaic generation system using the quasi-square inverter and to control the current quality using the PWM inverter. The proposal optimizes the system design, permitting the reduction of system losses and an increase of the energy injected into the grid.

Forecasting of the electric energy demand trend and monthly fluctuation with neural networks

Electric energy demand forecasting is a fundamental tool for production and distribution companies because it provides them with a prediction of the market demand of electric energy. Two kinds of forecasting may be performed: short term and medium to long term. This work is focused on monthly prediction, which is useful for the maintenance planning of grids and as market research for electricity producers and resellers. The timed series of monthly electric energy demands presents a rising tendency due to the influence of economic and technological evolution on the electric market. Embedded in this general trend is a fluctuation caused by the difference in demand from month to month. This paper proposes the extraction of that trend to perform separate predictions of both tendency and fluctuation with neural networks, which will be summed up to obtain the series forecasting. A Mean Absolute Percentage Error (MAPE) of about 2% has been obtained.

Comparison of two power flow control strategies for photovoltaic inverters

High penetration levels of distributed photovoltaic inverters on electrical distribution grids and due to the main points demanded in the latest issues of grid codes such as the reactive power injection during transient grid faults (voltage sags) and their disconnection in order to avoid the islanding phenomena, a suitable control algorithm combination is needed to provide an optimum operation at the electrical grids where they are connected, improving the power quality and the power supply continuity. This paper is devoted to the comparison through simulation of two power flow control strategies for 100 kW photovoltaic inverters in order to analyze the advantages and disadvantages of each one. The first one is based on the modulation index and the disphase controls of the reference signal face to the point of common coupling voltage. The second one is based on a synchronous reference frame with the voltage at the point of common coupling using id and iq current component controllers.

Hall-Effect Based Semi-Fast AC On-Board Charging Equipment for Electric Vehicles

The expected increase in the penetration of electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) will produce unbalanced conditions, reactive power consumption and current harmonics drawn by the battery charging equipment, causing a great impact on the power quality of the future smart grid. A single-phase semi-fast electric vehicle battery charger is proposed in this paper. This ac on-board charging equipment can operate in grid-to-vehicle (G2V) mode, and also in vehicle-to-grid (V2G) mode, transferring the battery energy to the grid when the vehicle is parked. The charger is controlled with a Perfect Harmonic Cancellation (PHC) strategy, contributing to improve the grid power quality, since the current demanded or injected has no harmonic content and a high power factor. Hall-effect current and voltage transducers have been used in the sensor stage to carry out this control strategy. Experimental results with a laboratory prototype are presented.

Overview of plug-in electric vehicles as providers of ancillary services

Proliferation of plug-in electric vehicles obviously involves an interesting potential of electric energy storage for purposes related to smart grids. Among other opportunities, the possibility of providing ancillary services to the distribution grids they are connected to is evaluated in this paper as an added advantage of this kind of vehicles. Issues like aggregation needs, incentives, coordination with the aggregation manager and generation of setpoints, as well as the charging/discharging constraints of PEV batteries depending on the task to be performed are addressed, as an overview of the most recent literature related to the subject.

A Smart Power Electronic Multiconverter for the Residential Sector

The future of the grid includes distributed generation and smart grid technologies. Demand Side Management (DSM) systems will also be essential to achieve a high level of reliability and robustness in power systems. To do that, expanding the Advanced Metering Infrastructure (AMI) and Energy Management Systems (EMS) are necessary. The trend direction is towards the creation of energy resource hubs, such as the smart community concept. This paper presents a smart multiconverter system for residential/housing sector with a Hybrid Energy Storage System (HESS) consisting of supercapacitor and battery, and with local photovoltaic (PV) energy source integration. The device works as a distributed energy unit located in each house of the community, receiving active power set-points provided by a smart community EMS. This central EMS is responsible for managing the active energy flows between the electricity grid, renewable energy sources, storage equipment and loads existing in the community. The proposed multiconverter is responsible for complying with the reference active power set-points with proper power quality; guaranteeing that the local PV modules operate with a Maximum Power Point Tracking (MPPT) algorithm; and extending the lifetime of the battery thanks to a cooperative operation of the HESS. A simulation model has been developed in order to show the detailed operation of the system. Finally, a prototype of the multiconverter platform has been implemented and some experimental tests have been carried out to validate it.

PSCAD/EMTDC model for photovoltaic modules with MPPT based on manufacturer specifications

Commonly used simulation models for photovoltaic arrays often require the knowledge of unavailable parameters and the calculation of equivalent resistances to be used. Those resistances are not provided by manufacturers and their calculation makes the application of the designed models difficult. This paper proposes a photovoltaic panel or array model based on the specifications provided by manufacturers in datasheets. The model emulates the characteristic curves declared by the panel manufacturer and an integrated MPPT (Maximum Power Point Tracking) technique. The well-known PSCAD/EMTDC software has been selected to develop the proposed model and commercial panels have been used for validation.

Power injection system for photovoltaic generation plants with active filtering capability

A single-phase power injection system for photovoltaic generation plants based in two collaborative inverters is presented in this paper. The first converter works as a quasi-square waveform inverter with the main objective of injecting the energy produced by the photovoltaic generation system. The second converter acts as an active power filter with the objective that the current that flows from or to the grid is sinusoidal. The presented power injection system will be able to achieve a sinusoidal current even when a non-linear load is connected to the same point of common coupling. The performance of the proposed system, including the maximum power point tracking algorithm, is evaluated through simulation.

Active and reactive power control strategies for electric vehicles in smart grids

Electric vehicles (EV) can be used as distributed energy storage devices to provide to the smart grid different regulation services. However, to have enough impact in the grid, the trend is to jointly manage a set of EV by means of a new stakeholder called aggregator. Knowing the availability, mobility habits, and the state of charge (SOC) of each EV battery, the aggregator performs the allocation of an active and/or reactive set-point to each EV within a time slot. This paper aims to propose reference charger currents for each EV from the individual set-point received by an aggregator. Control strategies for bidirectional active power control are compared under ideal and distorted source conditions. Besides, to contribute to reactive power support and voltage control in the bus where the set of EVs are connected, a reactive control strategy is proposed and validated by simulation. The proposed control strategies are applied to a Mode 2 single-phase EV battery charger connected to a single-phase grid. Simulation tests have been conducted using different control strategies and active/reactive set-points provided by the aggregator.