J O Pousinho

C Matias

J S Monteiro

Catalão
email: catalao@ubi.pt

G J Osório

H M I Pousinho

J C O Matias

C Monteiro

J P S Catalão

Hybrid Evolutionary Neuro-fuzzy Computational Tool to Forecast Wind Power and Electricity Prices

Keywords: Forecasting, computational tool, Wind power, Electricity prices

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Wind-generated energy is accepted as it comes (i.e. as it available) and wind-driven power resources have become increasingly important in planning and operations of power systems [START_REF] Ignácio | Comparison of Two New Short-Term Wind-Power Forecasting Systems[END_REF]. Portugal is no exception; indeed, it has one of the most ambitious goals, establishing 5100 MW of wind power installed by 2012.

However, the availability of the power supply generated from wind energy is not known in advance [START_REF] Catalão | An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal[END_REF]. Therefore, the integration of large share of wind power in electricity systems leads to some important challenges [START_REF] Kabouris | Impacts of Large-Scale Wind Penetration on Designing and Operations of Electric Power System[END_REF]. Wind power forecasting plays a key role in tackling these challenges [START_REF] Ernst | Predicting the Wind[END_REF].

In most competitive electricity markets, price series present the following features: high frequency, non-constant mean and variance, high volatility, high percentage of unusual prices, calendar effects, among other factors [START_REF] Nogales | Forecasting Next-Day Electricity Prices by Time Series Models[END_REF]. So, price forecasting is extremely important for all market participants for their survival under competitive environment [START_REF] Aggarwal | Electricity Price Forecasting in Deregulated Markets: A review and Evaluation[END_REF].

Short-term wind power forecasting is an extremely important field of research for the energy sector, as the system operators must handle an important amount of fluctuating power forms and the increasing installed wind power capacity. The time scaling, concerning short-term prediction, are in the order of some days (forecast horizon) and from minutes to hours (time step) [START_REF] Costa | A Review on the Young History of the Wind Power Short-Term Prediction[END_REF].

Hence, wind power forecasting and electricity prices forecasting represent two very important issues for the power systems sector.

Contribution to Value Creation

In the technical literature, several methods to forecast wind power have been reported, namely physical and statistical methods. A physical method has advantages in longterm forecast while statistical method does well in short-term forecast [START_REF] Ma | A Review on the Forecasting of Wind Speed and Generated Power[END_REF]. In the same context, several techniques to forecast short-term electricity prices have been reported, namely soft and hard computing techniques [START_REF] Aggarwal | Electricity Price Forecasting in Deregulated Markets: A review and Evaluation[END_REF]. Artificial intelligence approaches can be much more efficient computationally and as accurate as time series models, if the correct inputs are considered [START_REF] Rodriguez | Energy Price Forecasting in the Ontario Competitive Power System Market[END_REF].

This paper proposes a novel hybrid computational tool based on a combination of evolutionary particle swarm optimization (EPSO) with an adaptive-network-based fuzzy inference system (ANFIS), hereafter defined as EPA approach, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.

Proposed Approach

Evolutionary Particle Swarm Optimization

EPSO incorporates a selection procedure to the original particle swarm optimization (PSO) algorithm, as well as self-adapting properties for its parameters [START_REF] Valle | Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems[END_REF]. The general scheme of EPSO is the following [START_REF] Miranda | EPSO-Best-of-Two-Worlds Meta-Heuristic Applied to Power System Problem[END_REF], [START_REF] Miranda | Evolutionary Algorithms with Particle Swarm Movements[END_REF]:

• Replication: each particle is replicated r times.

• Mutation: each particle has its weights mutated.

• Reproduction: each mutated particle generates an offspring according to the particle movement rule. • Evaluation: each offspring has its fitness evaluated.

• Selection: by stochastic tournament the best particles survive to form a new generation. This scheme benefits in the right direction: -first, the Darwinistic process of selection and the particle movement rule. -Second, it is natural to expect that it may display advantageous convergence properties when compared with classical PSO [START_REF] Miranda | EPSO-Best-of-Two-Worlds Meta-Heuristic Applied to Power System Problem[END_REF].

Adaptive Neuro-Fuzzy Inference System

The ANFIS architecture is composed of five layers. Each layer contains several nodes described by the node function.

ANFIS is a class of adaptive multi-layer feed forward networks, applied to nonlinear forecasting where past samples are used to forecast the sample ahead. ANFIS incorporates the self-learning ability of neural networks (NN) with the linguistic expression function of fuzzy inference [START_REF] Yun | RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment[END_REF]. An adaptive network is functionally equivalent to a Sugeno-type fuzzy inference system.

Evolutionary Neuro-Fuzzy Computational Tool

This sub-section describes the EPA algorithm (Fig. 1) for wind power forecasting or electricity prices forecasting.

Step One: Form a matrix with a set of historical data (wind power or electricity prices), arranged in C columns of the same matrix.

Step Two: Select a number of columns of the previous matrix so that the set of values derived from it represents the input data.

Step Three: The selected values of the previous step can be submitted to the entrance of the ANFIS structure.

Step Four: Train the ANFIS structure with data of the previous step. The ANFIS structure uses a combination of least-squares method and back-propagation gradient descent method. The EPSO structure is used to tune the parameters associated with the membership functions of fuzzy inference system.

Step Five: Create a vector D, where D equals the number of membership functions, optimized by the EPSO algorithm.

Step Six: Define the parameters associated with EPSO algorithm. These parameters are provided in Table 1.

Step Seven: Extract the output data of the ANFIS.

Step Eight: The result of the forecast is obtained.

Forecasting Accuracy Evaluation

To evaluate the accuracy in wind power forecasting and electricity prices forecasting, the mean absolute percentage error (MAPE) is considered. The MAPE criterion is defined as follows:

ܧܲܣܯ = 100 ܰ |̂ - | ̅ ே ୀଵ () ̅ = 1 ܰ  ே ୀଵ ()
In ( 1) and (2), ̂ is the forecasted values and  is the actual values at period h, ̅ is the average values of the forecasting period, and N is the number of forecasted periods. In the case of wind power forecasting ܰ = 24, and in the case of electricity price forecasting ܰ = 168. The average price is used in (1) to avoid the adverse effects of prices close to zero [START_REF] Shahidehpour | Market Operations in Electric Power Systems: Forecasting, Scheduling and Risk Management[END_REF].

Case Studies

Short-Term Wind Power Forecasting

The proposed EPA approach has been applied for wind power forecasting in Portugal. The numerical results represented take into account the wind farms that have telemetry with the National Electric Grid (REN). Historical data are the only inputs for training the ANFIS. For a coherent and clear comparison, no exogenous variables are considered. The same test days as in [START_REF] Catalão | An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal[END_REF], [START_REF] Catalão | An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal[END_REF]- [START_REF] Pousinho | A Hybrid PSO-ANFIS Approach for Short-Term Wind Power Prediction in Portugal[END_REF], are selected, (July 3 and October 31 of 2007, January 14 and April 2 of 2008), corresponding to the four seasons of the year.

The predicted wind power series are held for 3 hours ahead, taking into account the wind power data of the previous 12 hours with a time-step of 15 minutes. This procedure is repeated until the next 24 hours values are predicted.

Numerical results with the proposed EPA approach in wind power forecasting are shown in Figs. 2 and3 for spring and fall days, respectively. Table 2 shows a comparison between the EPA approach and five other approaches: persistence, auto regressive integrated moving average (ARIMA), neural networks (NN), neural networks combined with wavelet transform (NNWT), and hybrid PSO-ANFIS (HPA), regarding the MAPE criterion. The EPA approach present better forecasting accuracy: the MAPE has an average value of 5.03%. Improvement in the average MAPE of the EPA approach with respect to the five other approaches is: 73.6%, 51.4%, 30.7%, 27.8% and 7.0%, respectively.

Short-Term Electricity Prices Forecasting

The proposed EPA approach has also been applied to forecast prices in the electricity market of mainland Spain. Price forecasting is computed using the historical data of year 2002 for the Spanish market, available at [START_REF]Market Operator of the Electricity Market of Mainland Spain, OMEL[END_REF].

Again for a coherent and clear comparison, no exogenous variables are considered. The same test weeks as in [START_REF] Conejo | Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA models[END_REF]- [START_REF] Amjady | Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm[END_REF] are selected, corresponding to the four seasons of the year. The predicted electricity price series are held for 168 hours ahead with a time-step of one hour, taking into account the historical price data of the six weeks (42 days) previous to the week whose prices are to be forecasted.

Numerical results with proposed EPA approach in electricity prices forecasting are shown in Figs. 4 and5 for winter and fall weeks, respectively. Table 3 shows a comparison between the EPA approach and four other approaches: wavelet-ARIMA, weighted nearest neighbors (WNN), adaptive wavelet neural network (AWNN), and cascaded neuro-evolutionary algorithm (CNEA), regarding the MAPE criterion. The EPA approach presents, again, better forecasting accuracy: the MAPE has an average value of 5.12%. The improvement in the average MAPE of the EPA approach with respect to the four other approaches is: 36.9%, 36.4%, 24.1% and 3.8%, respectively.

Computational Burden

In wind power forecasting or electricity prices forecasting, the average computational time required by the proposed EPA approach is less than one minute using MATLAB on a PC with 1GB of RAM and 1.8-GHz-based processor. Hence, the novel approach presents not only better forecasting accuracy, but also an acceptable computation time in both case studies.

Conclusions

This paper proposed a hybrid evolutionary neuro-fuzzy computational tool, based on combining EPSO and ANFIS (EPA approach), for short-term wind power and electricity prices forecasting. The application of the EPA approach is both novel and effective. In wind power forecasting the MAPE has an average value of 5.03%, and in electricity prices forecasting the MAPE has an average value of 5.12%. The computation time in both cases is less than 1 minute. Hence, the proposed EPA approach presents a good trade-off between forecasting accuracy and computation time in both case studies, taking into account results previously reported in the technical literature.

Fig. 1 .

 1 Fig. 1. Flowchart of the novel EPA approach.

Fig. 2 .

 2 Fig. 2. Spring day: actual wind power, solid line, together with the forecasted wind power, dashed line.

Fig. 3 .

 3 Fig. 3. Fall day: actual wind power, solid line, together with the forecasted wind power, dashed line.

Fig. 4 .

 4 Fig. 4. Winter Week: actual electricity prices, solid line, together with the forecasted electricity price, dashed line.

Fig. 5 .

 5 Fig. 5. Fall Week: actual electricity prices, solid line, together with the forecasted electricity price, dashed line.

Table 1 .

 1 Parameters of ANFIS and EPSO.

	Initialization
	Input DATA
	Training DATA
	Parameters of
	Membership
	Functions
	ANFIS	EPSO
	Convergence
	Inference Results	Classification

Table 2 .

 2 Comparative MAPE results for wind power forecasting.

		Winter	Spring	Summer	Fall	Average
	Persistence	13.89	32.40	13.43	16.49	19.05
	ARIMA [15]	10.93	12.05	11.04	7.35	10.34
	NN [2]	9.51	9.92	6.34	3.26	7.26
	NNWT [16]	9.23	9.55	5.97	3.14	6.97
	HPA [17]	6.71	7.22	4.59	3.13	5.41
	EPA	6.13	6.68	4.45	2.85	5.03

Table 3 .

 3 Comparative MAPE results for electricity prices forecasting.

		Winter	Spring	Summer	Fall	Average
	Wavelet-ARIMA [19] 4.78	5.69	10.70	11.27	8.11
	WNN [20]	5.15	4.34	10.89	11.83	8.05
	AWNN [21]	3.43	4.67	9.64	9.29	6.75
	CNEA [22]	4.88	4.65	5.79	5.96	5.32
	EPA	3.59	4.10	6.39	6.40	5.12

Acknowledgments

. This work is funded by FEDER funds (European Union) through the Operational Programme for Competitiveness Factors -COMPETE, and by Portuguese funds through the Fundação para a Ciência e Tecnologia -FCT, under Project No. FCOMP-01-0124-FEDER-014887 (Ref. FCTPTDC/EEA-EEL/110102/2009).

DATA Predicted Output DATA End Yes Yes No No


S Saraiva

R Melício

J C O Matias

C M P Cabrita

J P S Catalão
email: catalao@ubi.pt

Simulation and Experimental Results for a Photovoltaic System Formed by Monocrystalline Solar Modules

Keywords: Photovoltaic cells, modeling and simulation, experimental validation, iterative procedure

This paper focuses on the comprehensive modeling, simulation and experimental validation for a photovoltaic system formed by monocrystalline solar modules. The performance of the equivalent circuit model for a solar cell is validated by data measured parameters of photovoltaic modules. Also, this paper brings a novel iterative procedure to find the value of diode ideality factor, series and equivalent shunt resistances.

Introduction

Renewable energy resources will play a significant role in the world energy supply in the upcoming future. Solar energy is the primary source for other types of renewable energy, except tidal power and geothermal energy, and even the non-renewable and non-nuclear energies are coming from this primary source. It is accessed in almost all parts of the earth and has an excellent sustainability.

A photovoltaic (PV) system directly converts solar energy into electric energy. The main device of a PV system is a solar cell. Cells may be grouped to form arrays and panels. A PV array may be either a panel or a set of panels connected in series or parallel to form large PV systems [START_REF] Villalva | Comprehensive approach to modeling and simulation of photovoltaic arrays[END_REF]. Power-electronic converters are usually required to process the electricity from the PV device. Power-electronic converters may be used to regulate the voltage and current at the load, to control the power flow in grid-connected systems, and for implementing the maximum power point tracking (MPPT) on PV systems.

The solar cell is basically a semiconductor diode exposed to light. Solar cells are made of several types of semiconductors using different manufacturing processes [START_REF] Sedra | Microelectronic Circuits[END_REF]. The electrical energy produced by a solar cell depends on its properties and on the incoming solar radiation [START_REF] Eltawil | Grid-connected photovoltaic power systems: Technical and potential problems-A review[END_REF], known as irradiation.

The simplest equivalent electrical circuits approach is a current source in parallel with a diode [START_REF] Ishaque | Simple, fast and accurate two-diode model for photovoltaic modules[END_REF]. Some author's state that the solution with only a single diode model, i.e., current source in parallel to a diode, for modeling a PV system integrated into the electric grid is enough to obtain good results [START_REF] Luque | Handbook of Photovoltaic Science and Engineering[END_REF].

An improved version is the inclusion of a series resistance in the previous equivalent electrical circuit approach, and an extension of this version includes an additional shunt resistance, augmenting the number of parameters from four to five. This version is justified for modeling with improved accuracy and is followed in this paper. The Standard Test Conditions (STC) for solar cells supplies performance parameters under the standard solar spectral distribution at 1000 W/m 2 and cell temperature of 25 ºC.

Contribution to Value Creation

This paper uses an accurate mathematical model of a solar cell with five parameters as a tool to simulate the I-V characteristics of PV modules. The mathematical model for the solar cell with single-diode, series and equivalent shunt resistances is duly implemented in Matlab/Simulink. To test the validity of the model and validate the simulation results, comparisons between simulated and experimental results at STC are presented in this paper. The data measured from the PV modules correspond to a photovoltaic facility [START_REF] Giacobbe | Validação de modelos matemáticos de componentes de sistemas fotovoltaicos: Master Thesis[END_REF] at the Instituto Nacional de Engenharia, Tecnologia e Inovação (INETI). Moreover, this paper brings a novel iterative procedure to find the value of diode ideality factor, series and equivalent shunt resistances.

PV Model

The equivalent circuit model for a solar cell simulation with single-diode, series and equivalent shunt resistances, is shown in Fig. 1. The equivalent circuit model can be used to simulate an individual cell, a module, an array or a panel. In Fig. 1, G is the solar irradiance, I s is the photo generated electric current, I d1 is the current at diode D 1 , I p is the leakage current, R p is the equivalent shunt resistance, R s is the series resistance, I is the output current and V is the output voltage. The thermal voltage of the solar cell is given by:

Rs Is

q kT V T 1 1 = , ( 1
)
where q is the electron charge, k is Boltzman's constant, and T 1 is the cell temperature in Kelvin.

The diode saturation current at the operating-cell temperature is given by:

) 1 1 ( 3 * 1 * 01 01 1 * 1 1 ) T T ( T T k m q e I I - = ε , ( 2
)
where I 01 is the diode reverse bias saturation current, * 01 I is the diode reverse bias saturation current at STC, T* is the p-n junction cell temperature at STC in Kelvin, m 1 is the diode ideality factor, and 1 ε is the energy bandgap of the semiconductor.

The I-V characteristic associated with the model shown in Fig. 1 for the solar cell is given by:

p s V m I R V s R I R V e I I I T s + - - - = + ] 1 [ 1 1 ) ( 01 .
(

Equation ( 3) is an implicit equation: for the same irradiance and p-n junction temperature conditions, implies the use of an iterative equation to determine the output current in function of the output voltage, respectively where [START_REF] Jiang | Maximum power tracking for photovoltaic power systems[END_REF]:

oc V V so dI dV R = - = , ( 4
) sc I I sho dI dV R = - = . ( 5
)
For constant irradiance and p-n junction temperature conditions, the short circuit current I sc it is the greatest value of the current at the cell terminals. The short circuit current I sc is given by:

p sc s V m I R s sc R I R e I I I I T sc s - - - = = ) 1 ( 1 1 01 . ( 6
)
For constant irradiance and p-n junction temperature conditions, the open circuit voltage V oc is the greatest value of the voltage at the cell terminals. The open circuit voltage V oc , using Equation (3), is given by: ) 1 ln( 01 01

1 1 + + = I R V I I V m V p oc s T oc . ( 7
)
The diode ideality factor [START_REF] Dzimano | Modeling of photovoltaic systems: Master Thesis[END_REF] is given by:

I R V I I R V I V V I R V m - + - - - - - + = . ( 8
)
The output power is given by:

] ) 1 ( [ 1 1 ) ( 01 p s V m I R V sc R I R V e I I V P T s + - - - = + . ( 9
)
4 Iterative Procedure PV's maximum power point (MPP) varies with solar irradiance and temperature. Each V-I characteristic curve specify a unique operating point at which maximum possible power is delivered, the MPP, where the PV operates at its highest efficiency [START_REF] Jiang | Maximum power tracking for photovoltaic power systems[END_REF].

The data are: STC cell voltage for maximum power, current for maximum power, open circuit voltage, and short circuit current.

The goal for the novel iterative procedure proposed in this paper is to find the value of diode ideality factor, series and equivalent shunt resistances, with a very good approximation in the neighborhood around the maximum of the I-V curve, since this is the aimed operation point.

Hence, coinciding with the maximum power at STC is not fully imposed, nor at the cell voltage for maximum power at STC or the cell current for maximum power at STC, but the error is aimed at being very small.

In the proposed iterative procedure, the series resistance must be slowly incremented starting from a null value. Adjusting the I-V curve to match the cell reference condition requires finding the curve for several values of series and equivalent shunt resistances.

The Newton-Raphson method was used in the proposed iterative procedure due to the ability to overcome undesired behaviors [START_REF] Walker | Evaluating MPPT topologies using a Matlab PV model[END_REF].

The flowchart of the proposed iterative procedure to adjust the I-V output characteristics of the solar cell is shown in Fig. 2.

Plotting the I-V characteristic curves requires solving (3) for

] [0, sc I I ∈ and ] [0, oc V V ∈ . However, ) , ( I V f I = or ) , ( V I f V = are implicit equations.
Hence, numerical methods have to be used, imposing an adequate formalism. For instance, writing the I-V as

Simulation and Experimental Results

For each tested PV module, its characteristic curve is measured outdoors, quasisimultaneously with the measurement of the reference unit I-V curve. The I-V curve is then translated to STC conditions by using the procedure described in IEC 60891 [START_REF]Procedures for temperature and irradiance corrections to measured I-V characteristics of crystalline silicon photovoltaic devices[END_REF]. Both I-V curves are extrapolated to STC, according with the usual procedure [11]. The I-V curve tracer is shown in Fig. 3. The PV system formed by two monocrystalline solar panels series-connected is shown in Fig. 4. The monocrystalline technology modules tested were Isofotón I53. Table 1 summarizes the data for the silicon monocrystalline solar module Isofotón I53 at STC [START_REF]Isofotón Photovoltaic Products Information[END_REF]. In order to test the validity of the proposed model for the Isofotón I53, comparisons between the simulated and experimental results at STC were carried out. The I-V comparison is shown in Fig. 5. A very good agreement is verified between the simulated and the experimental results at STC, validating the mathematical model implemented in Matlab/Simulink and the novel iterative procedure proposed employing the Newton-Raphson method.

Conclusions

A solar cell model with single-diode, series and equivalent shunt resistances, is considered in this paper. The assessment of parameters by a novel iterative procedure employing the Newton Raphson method is proposed. The novel iterative procedure computes the main information regarding the use of this model. A very good agreement is verified between the simulated and the experimental results at standard test conditions.

Fig. 1 .

 1 Fig. 1. Equivalent circuit of the solar cell with single-diode, series and equivalent shunt resistances.

Fig. 2 .

 2 Fig. 2. Flowchart of the proposed iterative procedure to adjust the I-V output characteristics of the solar cell.

Fig. 3 .

 3 Fig. 3. The I-V curve tracer.

Fig. 4 .

 4 Fig. 4. PV system formed by two monocrystalline solar modules series-connected.

Table 1 .

 1 Solar module data for Isofotón I53 module.

Fig. 5 .

 5 Fig. 5. I-V comparison between the simulated model and the experimental results at STC.

Fig. 6 .

 6 Fig. 6. P-V comparison between the simulated and the experimental results at STC.

Acknowledgment. The authors gratefully acknowledge to Ms. L. Giacobbe for providing the measured data from the PV modules at INETI.


Nuno Freire
email: nunofr@co.it.pt

Eunice Ribeiro
email: eribeiro@co.it.pt

António Cardoso
email: ajmcardoso@ieee.org

Chiara Boccaletti
email: chiara.boccaletti@uniroma1.it

Experimental Analysis of a Standalone Renewable Energy Based Hybrid System

Keywords: Photovoltaic array, wind generator, battery, power converters

This paper presents a small-scale prototype of a wind-photovoltaic hybrid system, having batteries as energy storage components. All devices are connected to a common dc-bus using appropriate power converters. The photovoltaic modules are connected through a dc-dc buck converter using Perturb and Observe Maximum Power Point Tracking control. The permanent magnet synchronous generator (wind generator) power interface is composed of a three-phase diode-bridge rectifier followed by a buck converter. A bidirectional buck-boost converter is applied to the batteries in order to control the dc-bus voltage, to which the load is directly connected. The whole hybrid system was built as a laboratory prototype, by using a dSPACE digital controller. Several experimental results are presented in order to analyze the system performance under both steady state operation and transients.

Introduction

Renewable energy generators such as photovoltaic modules and wind generators have seen a great development during the latest decades and are currently considered mature and robust equipments. Their worst drawback is related to their dependence on climatic conditions resulting in an intermittent and unpredictable output power, which is not trustworthy for many applications. Therefore, their off-grid application always involves energy storage systems to match the power demand, overcoming the reduced generation by storing the surplus energy. To increase the reliability of such systems, different types of generators and energy storage components can be combined for an autonomous energy supply. Usually named as standalone power systems [START_REF] Boccaletti | Innovative Solutions for Stand Alone System Powering[END_REF], their reduced need of maintenance and fuel-free operation makes them attractive for a great number of applications, especially in remote areas, where providing a grid connection can be highly expensive or even unfeasible [START_REF] Vazquez | Procedure to Select the Optimised Power Architecture for a Telecommunications Network of Remote Units[END_REF]. Optimization of their sizing, simulation models and energy management are the greatest issues focused by research to improve the performance of these systems [START_REF] Nelson | Unit Sizing of Stand-Alone Hybrid Wind/PV/Fuel Cell Power Generation Systems[END_REF][START_REF] Shengtie | Coordinationation Control of Energy Management for Standalone Wind/PV Hybrid Systems[END_REF][START_REF] Valenciaga | Supervisor Control for a Stand-Alone Hybrid Generation System Using Wind and Photovoltaic Energy[END_REF]. Although power electronic converters play a crucial role in conditioning the intermittent and variable power supplied by the generators, they are usually assumed as standard topologies for these applications and they have not been properly addressed in the literature yet [START_REF] Patsios | Control and Analysis of a Hybrid Renewable Energy-based Power System[END_REF].

Throughout this paper a standalone renewable energy based hybrid system is presented and thoroughly analyzed, focusing on the components that dominate its performance: power electronic converters. An experimental setup was designed, built and tested. It includes a photovoltaic (PV) array, a permanent magnet synchronous generator (PMSG), a battery pack, and a digital controller that allows for real-time control and monitoring of all power converters. The main goal is to supply a DC load with a constant voltage of 50 V reliably. The obtained results show that the chosen power converters and respective control methods are suitable for this kind of systems, making clear the major role of them for the achievement of the desired overall system performance.

Contribution to Value Creation

The present work is a step further for the creation of reliable, sustainable and environmental friendly ways of providing energy without a grid connection. This is particularly useful for applications in remote places, such as, for instance, radio base stations, where to provide a grid connection can be quite expensive and the conventional gensets might have unwanted additional maintenance costs [START_REF] Vazquez | Procedure to Select the Optimised Power Architecture for a Telecommunications Network of Remote Units[END_REF]. Therefore, the study of power electronic converters for these systems contributes to the growth of economic value [START_REF] Vazquez | Procedure to Select the Optimised Power Architecture for a Telecommunications Network of Remote Units[END_REF].

Prototype Description

The hybrid system (Fig. 1) is composed of a PV array and a wind conversion system based on a PMSG as the main sources, a battery pack for energy storage and backup, and a resistive load. The main goal is to supply the load demand, keeping its input voltage constant at 50 V, using the intermittent energy provided by the generators, whose fluctuations are balanced by the batteries. All this components are connected through suitably chosen and designed dc-dc converters, which provide optimization and conditioning for each component output power.

The PV array is connected to the hybrid system through a buck DC-DC converter, as illustrated in Fig. 1. It controls the PV array output voltage, so that its maximum power point can be tracked using the Perturb and Observe algorithm, which is described in [START_REF] Hua | Implementation of a DSP-Controlled Photovoltaic System with Peak Power Tracking[END_REF]. This solution was chosen for its simplicity, maturity and efficiency. The employed wind generator is a permanent magnet synchronous generator (PMSG) and its three-phase output voltage is rectified by a diode-bridge rectifier which is followed by a buck dc-dc converter similar to the one applied to the PV array, as shown in Fig. 1. The PMSG electromagnetic torque is controlled through the buck converter by controlling the value of the current PMSG i . A buck-boost converter is responsible for keeping the load input voltage at 50 V ( ) DC V , through the appropriate control of the charging or discharging of the batteries. Therefore, the quantities required by the control system are: PMSG i , PV i , PV V and DC V (Fig. 1). The remaining quantities are only used for the analysis of the proposed hybrid system.

The batteries must be properly protected from overcharging and deep discharging. However, battery energy management issues are not the main concern of this paper and usually applied options such as dump loads and load shedding are not considered.

The experimental setup is shown in Figs. 2 and3. It includes a 2.2 kW PMSG coupled with a motor (PMSM) in a test bench (the latter emulates the spin of a wind turbine), a PV array composed of two series connected BP 4175 modules, a connection box for PV modules (PV laboratory interface -Fig. 3), a battery pack of 3x3 12 V 12 Ah lead acid batteries, Semikron IGBTs and diodes, eight voltage and current sensors, a dSPACE DS1103 digital controller and a variable resistive load. Tables 1, 2 and 3 present the PMSG, PV modules and converters parameters, respectively. As depicted in Fig. 2, all components have a modular structure, so that the system can be easily changed or redesigned.

Battery Pack

The control strategies were implemented in the DS1103 digital controller board, using a sample time of 20 µs. The switching frequency imposed through the PWM techniques was 6 kHz for the three dc-dc converters.

Experimental Results

Using the whole prototype setup previously described, several results were recorded and analyzed, using the dSPACE ControlDesk and Matlab/Simulink softwares.

Different operating conditions are considered in order to properly evaluate the system performance. On the one hand, under steady-state operation different battery operating modes were considered (charging/discharging). On the other hand, transient occurrences such as load and wind power generation changes were imposed.

The number of used PV modules was intentionally chosen so that the power provided by the PV array was not enough to supply the load. Therefore, by changing the PMSG load torque, it is possible to change the battery operation mode. Accordingly, for a reduced PMSG load torque, the battery will have to discharge to balance the load demand, whereas the battery will charge for a high PMSG load torque, since in this case the generated power (from both PV and PMSG generators) exceeds the load demand.

The PMSM drive controller is suitable for speed control. For all considered operating conditions a constant speed reference of 600 rpm was imposed. As a consequence, the PMSG speed is constant and its load torque can be controlled through the reference value for PMSG i . During all conditions, the battery together with its power interface keeps the load voltage constant at 50 V, as required, and balances the power production according to the load demand. The main goal of the analysis is to study the interactions between all the converters, their performance, and system stability.

Steady-State Operation

Figs. 4 and5 show all the acquired electrical measurements on the system for two distinct situations. As the PV array is not being emulated, its output cannot be changed, and its maximum output power (tracked by the converter control) remained almost constant at 118 W. Changes in the power produced by the photovoltaic array occur very slowly. On the contrary, the emulation of the wind turbine permits to introduce changes at any time and to study how the system reacts.

In Fig. 4, the results depicted are the load, generator, PV and battery voltages, currents and powers for a PMSG load torque equivalent to 17% of the rated torque. The power produced by both generators is not enough to supply the load demand. Therefore, the battery discharges while keeping the load voltage at 50 V as required. In Fig. 5, the same results are depicted for a PMSG load torque equivalent to 33% of the rated torque. By increasing the torque, the generated power increases as well. Thus, the power produced by both generators exceeds the load demand. This surplus energy recharges the battery. Its voltage slightly rises while charging as expected.

In order to analyze the dc-dc converters performance under steady-state operation, efficiency and voltage peak-to-peak ripple values were chosen as evaluation parameters. The overall dc-dc converters efficiency can be obtained for the two considered operating conditions as follows: In the first case (Eq. 1), all the sources supply the load, in other words, all the power flows to the load. In the second case, the power generated by the renewable sources flows to the load and to the battery, which means that it supplies the load and charges the battery. It is worth noting that the minus sign in Eq. 2 results from the adopted convention for the battery current, which is negative when the battery is charging. Consequently, the converters efficiency level calculation depends on the battery operating mode.

In both operating conditions, the overall dc-dc converters efficiency values are high and similar, showing a quite good performance. The obtained voltage peak-to-peak ripple values were similar for the two considered operating conditions. They are shown in Table 4. The values are quite good for any DC power application, such as water pumping, radio base stations, among others.

0.5 V 1 % VPMSG 0.4 V 0.4 % VPV 0.4 V 0.5 % VBat 0.3 V 0.8 %

Transients

The dynamic performance of the experimental setup for PMSG transients and varying load conditions are depicted in Figs. 6 and7, respectively.

Fig. 6 illustrates the electric parameters (voltage, current and power) of the generators, battery and load when such changes occur. At 0.13 s the PMSG load torque was increased from 17 % to 33% of the rated torque, by increasing the reference of PMSG i . As expected, the PMSG output voltage decreases and its current and generated power increase. This increase of the power injected into the dc-bus leads to an increase of dc V , which is immediately counteracted by the buck-boost controller in order to keep dc V constant. However, a slight increase of dc V causes an increase of PV V , resulting in a simultaneous reduction of the PV power. Following the stabilization of dc V , the PV power returns to its previous value and the system reaches the steady-state. In spite of the battery change to the charging mode, the output voltage of the battery remains almost unaffected, showing its slow dynamic response. In addition, Fig. 6 shows that the system transient response is relatively fast.

A load transient can be seen in Fig. 7. At 0.4 s a load reduction from 234 W to 129 W was introduced, by controlling manually a variable load resistance. Once more, a transient is responsible for a momentary variation of dc V , which is mainly reflected in the PV quantities. The PMSG outputs present a high immunity to this transient, remaining practically unchanged. Then, also for a load transient, the system presents a relatively fast dynamic response.

Conclusion

Taking advantage of the complementary nature of PV and wind generators, this paper proposes a hybrid system having these two as the main power sources. Although they are intermittent energy producers and dependent on the climatic conditions, the addition of a battery as an energy storage and backup balances any fluctuations. The addition of appropriate power converters and control strategies permitted to obtain an optimal and reliable operation of this system. The performed experimental setup allowed to study the power converters employed in a hybrid system including a PV array, a PMSG and a pack of batteries. A complete description of this hybrid system was presented. The parameters of all components were given and the control of the power converters was explained. The obtained results have shown a great performance of the whole system and a good efficiency of the power converters. Different transients were introduced and the system rapidly adjusted to new steady states. The voltage of the common dc-bus was successfully kept constant under several operating conditions and the load demand was always assured by the hybrid system, confirming the effectiveness of the latter in supplying smoothed power for any standalone application.

3 CFig. 1 .

 31 Fig. 1. Diagram of the standalone renewable energy based hybrid system.

Fig. 2 .

 2 Fig. 2. General view of the experimental setup.

Fig. 3 .

 3 Fig. 3. Detail of the PV array and its laboratory interface.

Fig. 4 .

 4 Fig. 4. Load, generator, PV and battery voltages, currents and powers for a PMSG load torque equivalent to 17% of the rated torque.

Fig. 5 .

 5 Fig. 5. Load, generator, PV and battery voltages, currents and powers for a PMSG load torque equivalent to 33% of the rated torque.

Fig. 6 .

 6 Fig. 6. Load, generator, PV and battery voltages, currents and powers during a PMSG transient.

Fig. 7 .

 7 Fig. 7. Load, generator, PV and battery voltages, currents and powers during a load change.

Table 1 .

 1 PMSG parameters.

	Power	2.2 kW
	Speed	1750 rpm
	Voltage	316 V
	Current	5.2 A
	Number of pole pairs	5
	Armature resistance	1.72 Ω
	Magnet flux linkage	0.244 Wb
	d-axis inductance	20.5 mH
	q-axis inductance	20.5 mH

Table 2 .

 2 BP 4175 PV modules parameters.

	Maximum power (Pmax)	175 W
	Voltage at Pmax	35.4 V
	Cur ent at Pmax	4.94 A
	Short-circuit current	5.4 A
	Open-circuit v ltage	44 V

Table 3 .

 3 System parameters.

	C1	0.75 mF
	C2	1.1 mF
	C3	2.4 mF
	L	10 mH
	R	10 -19.1 Ω

Table 4 .

 4 Voltage peak-to-peak ripple values.

	Peak-to-peak ripple
	Absolute Value	Relative Value
	VDC=VLoad
	Voltages

Acknowledgments. This work was partly supported by Fundação para a Ciência e a Tecnologia (FCT) under Project No. SFRH/BD/47741/2008, Project No. PTDC/EEA-EEL/114846/2009 and Project No. SFRH/BD/70868/2010.


Peteris Apse-Apsitis

Ansis Avotins

Leonids Ribickis

Janis Zakis

Development of Energy Monitoring System for SmartGrid Consumer Application

Keywords: energy efficiency, energy monitoring, signal processing, wireless communication

The number of electricity consuming equipment for existing household end-user is continuing to increase, and some residential buildings already consume more energy than existing building regulations prescribe. The uprising SmartGrid technology with alternative energy sources could be a key to solve this problem, but it is demanding also for a "smarter" consumer with ability to monitor and manage his loads. Such a monitoring system can also improve energy efficiency, as it can change consumer non-saving habits, by teaching him possibilities where the energy can be saved. Therefore the paper is devoted to development of new concept of household energy consumption monitoring system. Due to new approach of energy monitoring, the costs of needed metering equipment and total metering system are lowered. The proposed method provides energy consumption apportionment between consumers instead of precise energy consumption metering for each consumer type.

Introduction

Existing dwellings consume about 3 times more energy than it is prescribed in the current Latvian building regulations, which were developed before households became available with wide range of electrical appliances (see Fig. 1.), thus greatly contributing to the electrical load increase and network overloading. According to official data from local electrical energy supplier A/S Latvenergo and Riga Energy Agency [START_REF] Golunovs | Riga household specific electrical loads -planning instrument for improvement of energy efficiency[END_REF], comparing the year 2007 and 2003, the electrical energy consumption has increased by 11 %, and is expected to continue to increase. According to data of the A/S Latvenergo, consumption in 2010 industry sector grew by 5.3 %, but due to economical crisis and increase of electricity rates, consumption in households reduced by 3 %. Fig. 2 shows that small countries like Latvia and Estonia are facing the same increase in electricity consumption tendency as other countries all over the world [START_REF]Energy Information Administration (U.S.)[END_REF]. In order to achieve global goals of energy efficiency, additional economic and political stimulators will be needed to change non-saving energy consumer habits of conservative household, residential building in cities or rural region end-user. One of such economical stimulator is natural continuous rise of price for electricity, as it is connected with limited availability of fossil fuels. As the prices for electrical energy are increasing, and also availability of renewable energy sources in households, the idea of Smart Metering Systems and Smart Plugs in recent years got attention from both sides -energy supplier and consumer, as it could greatly contribute to energy consumption reduction, as shows research done by authors of articles [START_REF] Choi | Analysis of energy savings using smart metering system and IHD (in-home display)[END_REF][START_REF] Maity | Intelligent Online Measurement and Management of Energy Meter Data through Advanced Wireless Network, Devices and Communications (ICDeCom)[END_REF], by changing the habits of consumer, and thus creating more stable power grid in future.

But in order to solve this problem, the end-user must be informed about his possibilities to save energy, which could be reached by implementing smart metering systems with graphical indicators on screen, or visualization on PC with help and tips for possible solutions of energy consumption reduction possibilities of each consuming device.

Contribution to Value Creation

Each typical household or office has simple metering system as shown in Fig. 3. with just one electrical energy measuring device from electricity supplier. Regardless that there are many energy consumer types (P1...Pn) -TVs, music, fridges, microwaves, washing machines, heaters, boilers, computers, lighting (P2) etc, it shows just total energy consumption and without additional metering devices or special calculations, proper energy consumption of each consumer device cannot be obtained. To obtain precise energy consumption about each particular electrical device or group of them, additional metering devices are needed (see Fig. 3.), but consumer like lighting can be measured just at input. As the typical household nowadays has at least 8-10 regular electricity consuming devices, and as the digital metering devices, that can be plugged into electrical socket cost around 16-30 euro (EUR), the investment of 171 to 285 EUR for such monitoring can be questionable for typical end-user. Another problem is that typical end-user does not have educational background to properly understand meanings of W, VA, VAr, kW, kWh, A, V and cos φ values visualized on the typical digital socket metering device display, thus the main focus should lie on how much "EUR" is consumed right now and in time period. Thus it can be concluded, that in order to "smarten" or convince end-user to use smart meters, they should be cheaper and able to give much direct and understandable message to end-user. Technically metering device should have also small dimensions, so that it could be integrated into back of wall socket.

Concept of Proposed Energy Monitoring System for End-User

The main idea is to provide energy consumption apportionment between consumers instead of precise energy consumption metering for each consumer type, as it could decrease the price of measuring elements. The distance between "central measurement point", and "monitoring point" typically is less than 100 m, the voltage u(t) practically is the same for each consumer and energy consumption can be characterized by just monitoring each consumer current i(t) value, and central measurement point, which makes precise measurements of u(t), i(t) true root mean square (rms) values, receives relative current values from monitoring device, via wireless or power line communications, and makes indicative visualization of energy consumption per consumer on display or sends it to PC. Here 2.5 % -5 % precision is enough for monitoring task and such precision corresponds to 80 sec or 180 sec consumer state "ON". Energy consumer power typically is within range from 10 W up to 2250 W (up to 10 A), consumers have R or RL (also RC) load characteristics, current and voltage graphs are sinusoidal under normal conditions. In order to get measurements and relative current value distribution in total energy consumption, the central measurement point is measuring precisely both voltage and current values, and measurements are synchronized in time with each monitoring measurement point, which measures only current value, at the exact monitoring point.

Power Measurement and Calculation Methods

Electrical power contains active and reactive components and they correspond to each other via cos φ. Energy supplier places bill just for active component consumption in the most of cases for individuals and legal persons, for example in Latvia charges for reactive energy, when tg φ is greater than 0.4 (cos φ <0,929) and with allowed load 100 kW and more, is additional 0.003 Ls/kVArh. Therefore just active power measurement and visualization to end-user is actually needed. shows that in sinusoidal consumer cases or in cases of consuming devices that are equipped with power supplies with Power Factor Correction circuit, possible to determine that active current max value (amplitude) is full current value measured at 1/4 period (90 full current value measured at 1/2 period (180 solution and operation diagrams sinusoidal current consumer case, in more detail is described in previous research of authors [START_REF] Apsitis-Apse | Concept of Low-Cost Energy Monitoring System for Household Application[END_REF]. The key element is wireless signal transceiver, where to fulfill the low cost problem, a radio frequency transceiver with SC2262 encoder and SC2272 decoder is chosen.

Sinusoidal Current

Non-sinusoidal C

In case of non-sinusoidal current consumers like power supplies, it is obvious, that a for sinusoidal current consumers periodical load alternating current power consumption. Overall known expression determine relationship between DC supply power, current and voltage: Development of Energy Monitoring System for SmartGrid Consumer Application urrent Consumers current consumers, active power values can be calculated from cos

P U I ϕ = × × . [1 cos(2 )] sin( ) sin( ) 2 m m m m m I U t P I t U t ω ω ω × × - = × × × = .
Voltage, active, reactive and full current graphs, where a) cosφ=0,5; Fig. 5. shows that in sinusoidal consumer cases or in cases of consuming devices that are equipped with power supplies with Power Factor Correction circuit, possible to determine that active current max value (amplitude) is full current value at 1/4 period (90 o ) or 3/4 period (270 o ) and reactive current max value is full current value measured at 1/2 period (180 o ) or period (0 o or 360 o ). The technical peration diagrams of electrical consumption monitoring device current consumer case, in more detail is described in previous research of ]. The key element is wireless signal transceiver, where to fulfill the low cost problem, a radio frequency transceiver with SC2262 encoder and SC2272

Current Consumers

sinusoidal current consumers like average PC or laptop (see Fig. 6) ies, it is obvious, that a different power calculation method is needed for sinusoidal current consumers. Instant energy calculations are used to determine periodical load alternating current power consumption. Overall known expression determine relationship between DC supply power, current and voltage: Development of Energy Monitoring System for SmartGrid Consumer Application 351 ctive power values can be calculated from [START_REF] Golunovs | Riga household specific electrical loads -planning instrument for improvement of energy efficiency[END_REF] (2) =0,5; φ=60; b) Fig. 5. shows that in sinusoidal consumer cases or in cases of consuming devices that are equipped with power supplies with Power Factor Correction circuit, it is possible to determine that active current max value (amplitude) is full current value ) and reactive current max value is The technical electrical consumption monitoring device, for current consumer case, in more detail is described in previous research of ]. The key element is wireless signal transceiver, where to fulfill the lowcost problem, a radio frequency transceiver with SC2262 encoder and SC2272 (see Fig. 6) different power calculation method is needed than gy calculations are used to determine periodical load alternating current power consumption. Overall known expression Where ܲ ௗ -DC load power consumption, load resistance. DC load energy W consumption consumption over time period: AC load energy W for sinusoidal and non applied for symmetrical AC current consumption graph (see Fig. 7). replaced and characterized by summary value of discrete energy values over half period.

ܹ ൌ
Here we are talking about power consumption monitoring or about percentage of each consumer power consumption in overall power consumption. itis et al.

ܲ ௗ ൌ ሺܫ ௗ ሻ ଶ ൈ ܴ ௗ .
DC load power consumption, ܫ ௗ -DC load current, ܴ ௗ DC load energy W consumption is characterized as power consumption over time period:

ൌ ܲ ௗ ൈ ܶ ൌ ሺܫ ௗ ሻ ଶ ൈ ܴ ௗ ൈ ܶ .
Waveforms of consumed voltage and current of PC (on left) and laptop (on right energy w consumption also is characterized as power consumption instant AC current values:

ݓ݀ ௧ ൌ ሺ݅ሻ ଶ ൈ ܴ ௗ ൈ ݐ݀ .
Half period calculations for symmetrical non-sinusoidal current consumption.

is integrated instant energy value dw ୲ over AC half for sinusoidal and non-sinusoidal current forms. Half-period integration can be applied for symmetrical AC current consumption graph (see Fig. 7). Integration can be replaced and characterized by summary value of discrete energy values over half

ܹ ൌ  ݅ ௫ ଵ/ଶ ݐ݀ ൎ ∑ ݅ ௫ భ .
e are talking about power consumption monitoring or about percentage of each consumer power consumption in overall power consumption. half-period T period integration can be Integration can be replaced and characterized by summary value of discrete energy values over half [START_REF] Stein | Direct Energy Feedback Technology Assessment for Southern California Edison Company[END_REF] e are talking about power consumption monitoring or about percentage of

Technical Solution

The power measurement block diagram Fig. 8. As it can be seen, a is used. Zero-cross detection is needed for detecting half special design rectifier based on operational am applications, to transfer AC signal as DC signal directly to microprocessor, which is controlling transmitter and receiver for wireless data exchange between central measurement point and monitoring points. Copper Rshunt (1) and Rreference resistor (2) winding.

Shunt was used as current sensor (see Fig. 9), and dissipated power in shunt must be taken in to account and this fact reduce shunt value and make more difficult to read voltage drop on it for low consumer energy (power). Unfortunately other current sensors like Hall effect are more expensive, typically 3-8 EUR/piece and cannot be applied in this case, as the goal is to get low-cost measurement device, the shunt M1WSJ8M0U, PMR50HZPJU8L0, or similar, has price from 0,30 00 EUR, which is also significant price [START_REF]WEB catalogues[END_REF]. To decrease current measurement circuit price ordinary copper wire (see Fig. 9.) and resistance controlled gain of the amplifier was chosen instead. Ordinary copper wire is characterized by resistivity at 20 deg/C) and temperature coefficient 0.0039 K -1 . So, for example, varies from 0.00310 Ω (at 0 deg/C) to 0.00388 Ω (at 60 deg/C more than 25%, thus resulting incorrect measurements. To exclude this reference resistor to control amplifier gain is applied. Reference resistor is made from copper and is close to shunt in the same housing. , and rectifier is plifiers, widely used in audio transfer AC signal as DC signal directly to microprocessor, which is controlling transmitter and receiver for wireless data exchange between central ), and dissipated power in shunt must be taken in to account and this fact reduce shunt value and make more difficult to read voltage drop on it for low consumer energy (power). Unfortunately other current and cannot be cost measurement device, the shunt M1WSJ8M0U, PMR50HZPJU8L0, or similar, has price from 0,30ent measurement and resistance controlled gain of the Ordinary copper wire is characterized by resistivity ρ o, for example, at 60 deg/C), or . To exclude this, a reference resistor to control amplifier gain is applied. Reference resistor is made from For data processing, storage and computing tasks, a system described in previous research of authors [START_REF] Apsitis-Apse | Concept of Low-Cost Energy Monitoring System for Household Application[END_REF] can be implemented. Most of the systems available on market [START_REF] Stein | Direct Energy Feedback Technology Assessment for Southern California Edison Company[END_REF][START_REF]WEB catalogues[END_REF] (price varies from 14-429 EUR) can be used to monitor energy consumption, but not in Smart Grid context, as they don't allow sending collected data to user (PC) and as feedback to energy distributing company.

Fig. 1 .

 1 Fig. 1. Availability of electrical equipment in households in Latvia.

Fig. 2 .

 2 Fig. 2. World total electricity consumption 1980-2006.

Fig. 3 .

 3 Fig. 3. Existing metering system.

Fig. 4 .

 4 Fig.4. Block diagram of proposed energy monitoring method.

Fig. 5 .

 5 Fig.5. Voltage, active, reactive and full current graphs, where a) cos cosφ=0,866; φ=30.

Fig. 5 .

 5 Fig.[START_REF] Apsitis-Apse | Concept of Low-Cost Energy Monitoring System for Household Application[END_REF]. shows that in sinusoidal consumer cases or in cases of consuming devices that are equipped with power supplies with Power Factor Correction circuit, possible to determine that active current max value (amplitude) is full current value measured at 1/4 period (90 full current value measured at 1/2 period (180 solution and operation diagrams sinusoidal current consumer case, in more detail is described in previous research of authors[START_REF] Apsitis-Apse | Concept of Low-Cost Energy Monitoring System for Household Application[END_REF]. The key element is wireless signal transceiver, where to fulfill the low cost problem, a radio frequency transceiver with SC2262 encoder and SC2272 decoder is chosen.

Fig. 6 .Fig. 7 .

 67 Fig.6. Waveforms of consumed voltage and current of PC (

Fig. 8 .Fig. 9 .

 89 Fig.8.

Fig. 8 .

 8 Fig.8. Diagram of technical solution for monitoring point.

2 1

 2 Development of Energy Monitoring System for SmartGrid Consumer Application 353 is shown in for current measurement

Table 1 .

 1 Temperature Influence on Shunt Resistor.

	Temperature,	Amplifier output without	Amplifier output with
	o C	compensation, mV	compensation, mV
	30	19.2	17.38
	60	21.4	17.7
	Difference, %	10.3 %	1.8 %

Conclusions

Described method allows designing and implementing low cost consumed energy monitoring for several consumers. Achieved price level is 4.55-4.70 EUR per measuring point and 29.88 -32.77 EUR per main measurement point unit, with overall costs tending to be less than ~71 EUR and it is acceptable for average household or small office. Further tasks are a development of communication protocol and interface between main measurement point and meter of energy distributing company. Proposed system has been submitted to the European Patent Office, thus further detailed description of the system will be available in authors next scientific papers.


Rui Medeiros

Stanimir Valtchev

Svilen Valtchev

The Efficient and Stable Charging of Electric Vehicle Batteries: Simplified Instantaneous Regulation

Keywords: Electric/Hybrid Vehicle, Battery, Charging, Contactless Energy Transfer, Series-Loaded Series-Resonant Converter

The future charging of the growing fleet of Electric Vehicles (EV) requires new solutions that guarantee a better efficiency and widely spread chargers. Both in fast charging and in slow charging of the electric vehicle (EV) and hybrid electric vehicle (HEV), the wireless charger is the better choice. The wired charger is limited by its cabling and relatively lower output voltage, while the contactless charger can be more universal and safe. What rests to be discovered is the more efficient energy transfer that would have a stable operation. The strategy is based on guaranteeing the correct portion of energy transported by the resonant tank to the load. Some results are shown from this implementation of the instantaneous energy control.

Introduction

The most problematic building block of any autonomous electric driven vehicle is its electric energy source. The battery defines the most important characteristics of the vehicle: the total available energy (i.e. its autonomy) together with the achievable velocity and acceleration (i.e. maximum instantaneous power).

For example, the Li-Ion battery of 25kWh can guarantee 200km of autonomy but its weight is at least 150kg and costs at least 12000€. In parallel, a fuel tank of 50 litters will be sufficient for 900 or more km autonomy. The electric motor has advantages they are not sufficient to compensate for the battery disadvantages: heavy, expensive, short life, etc. The lack of suitable batteries is the reason for the HEV and Plug-in HEV to exist. In case of HEV and especially PHEV the battery is smaller and the speed (the power) of charging is usually limited to the household grid capacity. It is not easy to decide if fast or slow charging will be adopted. The internal combustion engine habits are strongly influencing the thinking of the consumer but it is predictable that a slower and more regular charging will be more efficient (lower power required, longer battery life, smart grids, etc.). The contactless charging is capable to offer higher power charging solutions but it is not so attractive to apply almost megawatt power to succeed in charging for 5 minutes. To make clearer how important is the speed and the depth of discharge of the battery, SAFT provided diagrams based on measurements on real EV batteries (Figs. 1 and2). Similar results are known for the charging of batteries.

The life expectancy of the battery (defined here as the moment when the capacity of the battery diminishes by 20%) is seen to be halved (Fig. 1) if the depth of discharge of each cycle (DOD) changes from 50% to 80%, i.e. from 4900 charging/discharging cycles to 2300 cycles. The internal resistance is also doubled in the same number of cycles: slowly when the battery is treated by DOD = 50% and rapidly if DOD = 80%. The available capacity of the battery, shown in Fig. 2, is also becoming lower when high speed of discharge is applied (i.e. forcing the chemical processes). The curves in Fig. 2 correspond also to Peukert's equation in general: the useable capacity diminishes if the charge/discharge currents go higher.

Another factor limiting the capabilities of the fast charging devices is the grid, which should in this case supply huge power levels in inconvenient place and time. To use the cars as energy storage in smart grids in case of fast charging is also improbable: high peaks of energy transfer are inconvenient. The slower (safer) charging station is supposed to use the pattern shown in Fig. 3, where it is seen that the initial charging process runs at a fixed current of 12A, and later the current goes exponentially down while the voltage is kept constant.

Contribution to Value Creation

Many contributions were made in the last years to improve the efficiency and the controllability of the resonant converters [START_REF] Rossetto | A Simple Control Technique for Series Resonant Converters[END_REF][START_REF] Souesme | Study of a Control Method to Gain the Best Dynamic Performances of the Series Resonant Converter[END_REF][START_REF] Kim | A New State Feedback Control of Resonant Converters[END_REF]. The interest in wireless power transmission has been growing for the last two decades, especially because the technology is now advanced enough to implement it.

This work proposes a further improvement to the simplified method to control the energy transported by the resonant tank(s) to the load (EV battery), reported in [START_REF] Valtchev | Improved Strategy for an Instantaneous Super-Resonant Converter Regulation[END_REF]. This new Instantaneous Control is similar in its essence to the classic CM (Current-Mode) Control [START_REF] Ridley | A New Continuous-Time Model for Current-Mode Control[END_REF], but in the new method the internal variable used to control the resonant processes is the resonant capacitor voltage instead of the inductor current. The method proved to be valid in simulations [START_REF] Valtchev | Improved Strategy for an Instantaneous Super-Resonant Converter Regulation[END_REF], followed now in measurements. It appears simple, fast and efficient.

Contactless Charging

The transfer of the charge to the battery is the most important technological problem of the EV (or of some other autonomous, e.g., robotic device). This problem is closely connected to the speed of charge, to the feedback information from the battery, etc.

A solution could be to apply a wireless (e.g. Inductively Coupled) energy transfer where cables are not required and the secondary rectifier can be connected by fixed and better cooled copper bars. The wireless energy transfer is a credible solution for the charging as it permits a safer energy interface with the vehicle even during bad weather, careless manipulation, etc. The chargers can be aimed either at a static position of the EV in relation to the transmitter of energy, or to the dynamic position. For the moment, the static charger is expected to have wider penetration into the market but the dynamic charging has some (the infrastructure is expensive and less efficient) support from the engineers. One example of dynamic charger is the OLAV project in Republic of Korea (Fig. 4). The dynamic contactless power transfer is already applied in the industry, e.g. for robots at a fixed route. In both its static and dynamic version, the contactless power transfer can be (in theory) bi-directional. This requires of course, a more sophisticated secondary side but makes possible the energy management of the grid.

The Series Resonant Topology for Contactless Charging

The main topologies for wireless energy transfer are always resonant. The versions may include one or two resonant loops, each one series or parallel, differing also by the way the load is connected. Here the main topology will be the series resonant converter although the other versions are kept in mind. This version involves a series connection of L and C elements and in terms of resonant frequency stability, this is the better solution. This resonant topology is characterized by higher amplitudes of the resonant current, which is not so difficult to cope with. The modern switching power devices have enough reserve in maximum allowed current value (the breakdown voltage is still a limitation, especially when low voltage drop and high speed at high current are required).

The Regulation of the Charge in a Resonant Topology

Energy Balance

An excitation voltage V LC is applied to the series resonant loop LrCr and can be represented as the instantaneous difference between the input and output voltages v s and v o (Fig. 6). In case of loosely connected magnetic link the same figure corresponds to the recalculated primary [START_REF] Valtchev | Efficient Resonant Power Conversion[END_REF][START_REF] Valtchev | Resonant Contactless Energy Transfer with Improved Efficiency[END_REF]. The excitation voltage V LC applied across the LC tank (v(C r )+v(L r )) is V LC1 >0 during the interval ψ 1 (Fig. 7) and V LC2 <0 in the interval ψ 2 . In some equations and in Fig. 8 (for convenience) the normalized (angular) variables ψ 1 , ψ 2 and ωt, are substituted with the time variable t.

In a steady-state operation the energy LC ∆E E E E of the LC tank equals zero in the beginning and the end of each half period of switching. The current increases from zero and charges energy to the tank until t 1 (Fig. 8) and this energy is discharged after t 1 until the end of the half switching period:

1 1 2 1 2 0 0 sw T t LC LC r LC r t V i dt V i dt ∆ = + = ∫ ∫ E E E E (1)
The energy increase LC ∆E E E E is defined as zero as the energy portion extracted from the input voltage source E s is equal to the energy portion delivered to the output voltage source V o . If it is necessary to increase the energy in the tank, then the circuit will be commanded by a longer conduction interval ψ 1 than the necessary for its steady state, and hence LC ∆E E E E will be positive. When the energy must diminish, then ψ 1 is decreased and

LC ∆E E E E will be negative.
At the zero crossing points of the current (Figs. 7,8) the total resonant tank energy consists only of resonant capacitor energy

2 max 2 r c C v
, so the capacitor voltage maximums v cmax can be used to assess that energy [START_REF] Valtchev | Efficient Resonant Power Conversion[END_REF]. In the case of contactless power transfer, the converter operation is described by the same expression (2) where v cmax1 and v cmax2 correspond to the resonant capacitor voltage amplitudes in the primary [START_REF] Valtchev | Resonant Contactless Energy Transfer with Improved Efficiency[END_REF]. In that case, the normalized output voltage q in (2) will be substituted by the corrected value T q K q = , where K is the magnetic coupling factor of the loosely coupled magnetic link (transformer).

N C 1 max1 max 2 (0, ) 2 ( ) ( ) 2 sw c c c T v t q v v ∆ = - + E (2)

Simplified Strategy for Regulation

In the hard switching converter with Current Mode regulation, the inductor current is the instantaneous power indicator for the energy transferred each half period in the SLSR converter. Considering that the resonant processes are not so simple, the Current Mode regulation is not directly applicable in the SLSR converter. For example, the peak value of the resonant current does not correspond to the maximum stored energy in the resonant loop.

In contrary to the hard-switching converter, the SLSR converter shows output characteristics similar to a current source as shown in [START_REF] Valtchev | Efficient Resonant Power Conversion[END_REF][START_REF] Valtchev | Some Regulation Characteristics of Pulse-Width Modulated Series Resonant Power Conversion[END_REF]. In this case, the internal variable is more proper to be the resonant capacitor voltage v c which reflects proportionally the integrated resonant current during each half-period of switching. The value of this variable will correspond to the size of the energy circulating in the resonant tank. The commutation command will be produced when the measured resonant capacitor voltage becomes equal to a previously calculated value v c (t 1 ), if this calculation guarantees (predicts) the final value v cmax2 . From the regulation point of view, the only controllable variable that the power switches can commutate is the current through the transistors. In the same time, by controlling the current conduction, the transistors control the charge of the resonant capacitor. The total energy cannot be easily obtained in every moment, but it can be measured in the points of resonant current zero crossings.

The SLSR converter keeps its steady-state operation during many switching periods if there is no variation of the load and no change of the input power source values. In that case the v cmax1 and v cmax2 are equal and transistor must switch off at:

1 max ( ) c c v t qv = (3)
The plots in Fig. 9 illustrate the steady-state equation (3). To keep the amplitudes of the resonant capacitor voltage v cmax unchanged, it is necessary to keep the switching off at its level defined by [START_REF] Nguyen | An Implementation of Current-Mode Control for a Series-Resonant DC-DC Converter[END_REF]. The regulation method will require a calculation by multiplying the normalized output voltage q and the last measured amplitude voltage of the resonant capacitor.

Fig. 9. Switching off of the resonant current in SLSR converter

The transition process from a lower output power consumed in an initial steadystate, to a higher level of output power, will require several portions of additional energy supplied by the resonant tank. The amplified error voltage (proportional to the difference between the really obtained and the required output voltage) will define the necessary portions of energy for the transition (similarly to the Current Mode Control).

A higher consumption of power at the output corresponds to a positive increment

LC ∆E E E E
in the energy portions. The control action is to produce the turn-off of the transistor (or diagonal) at a certain level

c new c c v t qv qv = + ∆ = E (4)
The expression (4) can be simplified in order to suit better the practical implementation. The (normalized) energy increment N LC ∆E is further expressed in (5) as a voltage increment ∆v cmax1 :

1 max1 max1 max1 max1 ( ) ( ) c new c c c c v t qv q v q v v = + ∆ = + ∆ (5)
The calculation of ( 5) consists in memorizing the amplitude value of the resonant capacitor voltage v cmax1 . This value is kept until the next amplitude is read, i.e. v cmax2 . During this "hold" time the value v cmax1 is multiplied by q. After that the voltage q∆v cmax (proportional to the output voltage error signal) is added. The calculated level v c (t 1 ) new is delivered to the comparator. When the resonant capacitor voltage reaches the reference v c (t 1 ) new the transistor current is switched off similarly to the process illustrated by Fig. 9. After the switching-off the resonant capacitor voltage is expected to reach the new amplitude value v cmax2 . The process is limited by the already developed previous v c waveform, so the maximum v c (t 1 ) new value (delivered to the comparator) cannot be higher than the initial value v cmax1 . This means that the full transition process will need several half periods (at their maximum stretch). This method is better described and simulations are proposed and tested in [START_REF] Valtchev | Improved Strategy for an Instantaneous Super-Resonant Converter Regulation[END_REF].

Experimental Verification

A pre-existent autonomous electric vehicle, property of HOLOS SA., was used in tests. Its power demands are lower than 250W. This relatively small vehicle is equipped with 2 sets of batteries, each one corresponding to 24V and 12Ah. The robot electric charge is made with the conventional solution which includes wires. The idea is to adopt contactless charging in the near future.

The converter applied for the test was based on a ready-made MOSFET converter for piezoelectric actuators rated 2kW, produced by APRONEX Ltd. [START_REF] Apronex Ltd | High Frequency Generator for piezoelectric actuators[END_REF]. The experimented power circuit (Fig. 10) is slightly different from the shown in Fig. 5, since it is based on a half-bridge structure. The half-bridge topology should be in this case more convenient considering the necessity for resonant capacitor voltage measurements, but seen from another point of view, the resonant current is supplied by two different sources alternatively: the filter capacitor C1 and filter capacitor C5. Those two power sources do not guarantee symmetry in the circuit functioning as any asymmetry in the (steady-state) process of energy exchange through the LC link is reflecting in asymmetrical charge of the filter capacitors and hence in more asymmetry.

In each half period during the time interval t 1 (from Fig. 8), electrical charge is taken from one of the filter capacitors, but some part of the same charge is returned to the other filter capacitor during t 2 . The result is fluctuation of the voltage in K (Fig. 10) in relation to the bipolar supply voltage (310V) and some difficulty in defining the proper values of the resonant capacitor voltage. The converter transformer (Main Transformer in Fig. 10) was rebuilt in the laboratory especially to be used in contactless charging of vehicles and has relatively low magnetic coupling. Fig. 11 shows the assembly achieved and the waveforms obtained. The fact that magnetic coupling coefficient is lower than 1 in contactless energy transfer must be consider in the transformer design. Moreover it is important to minimize the abrupt fall of magnetic coupling with the slightly misalignment that will probably occur between the primary and the secondary sides of the transformer.

Fig. 11. Complete laboratory assembly with APRONEX MOSFET converter already modified

A conic shaped transformer (as in Fig. 15) was considered as a solution to align the robot (and the secondary in it) with the primary on the floor or applied in a wall. The electric vehicle would have installed a 'nose' and the docking would take place naturally if the primary winding was itself a cone placed, for example, in a wall. The magnetic coupled obtained was too low and the idea was abandoned. Planar windings were then adopted. Planar ferrites were glued in aluminum rectangular tubes to support the windings. Aluminum here intends to absorb magnetic field leaks as a measure of protection. Fig. 12 shows the primary side of the transformer with 27 turns. The secondary is similar, but it is built with 3 turns (9:1).

Fig. 12. Project of primary winding

Fig. 13. Primary winding with arrows indicating current direction and sense Fig. 13 shows arrows indicating the current sense. This '8' direction intends to create a magnetic arc perpendicular to the plates so the misalignment of the transformer will be less problematic. In the near future tridimensional simulations will be presented. New winding shapes will be tested with the main objective of increase the magnetic coupling. The converter transformer final result is visible in Fig. 14. It is working well but in the near future will be improved by the supplied by APRONEX Litz wire.

A new converter (full bridge) is expected also to be supplied from APRONEX or built in the laboratory in the near future. This will improve the voltage and current shapes shown in Fig. 16. The undesirable inductances of the connection cables and the slow response of the intrinsic anti-parallel diodes of the MOSFETs contributed to some flawed waveform of the current (inductive jumps in the measurements).

Regardless of all the difficulties in measuring the state variables, the obtained results are conclusive in proving the idea of instantaneous (energy balanced) control. In Fig. 16 are illustrated the waveforms measured in the resonant tank. The moment in which the current i(L) is switched from the transistor to the opposite anti-parallel diode is marked by the arrow. This switching is also well visible because of the inductive reaction of the measurin voltage on each of the dou voltage is

V v o 6 , 11 =
, as the measured value from the electronic load shows in Fig. obtained by maximal approximation between the primary and secondary ferrite plates and is roughly k = 0.95. In this case the non normalized voltage in the resonant capacitor voltage at the switching off moment must be 0.865*74V=64V. In fact the moment of switching off is at a capacitor voltage of 64 volts.

Conclusions

Because of the meaning already attributed to the expression "voltag in the past, the presented above resonant (SLSR) converters. The transformer ratio (if it were ideally coupled transformer) should correspond to the number of turns of the primary winding divided by the secondary winding turns, i.e. 9:1. It should be expected the normalized output voltage q to be The transformer here is not ideal. The maximum coupling is obtained by maximal approximation between the primary and secondary ferrite plates = 0.95. In this case the non-ideal magnetic coupling transforms the normalized voltage in q T [9,10], i. . Following ( the resonant capacitor voltage at the switching off moment must be Because of the meaning already attributed to the expression "voltage-mode" control in the past, the presented above method was called "instantaneous" control of the R) converters. It permits the internal processes of the SLSR converter to ontrolled faster and with precision. This control of the SLSR converter shows in the good results that the simulation has shown in [START_REF] Valtchev | Improved Strategy for an Instantaneous Super-Resonant Converter Regulation[END_REF]. In case of contactless energy converter the control permits to include the magnetic coupling another variable, observed and included into calculation. It is also possible to obtain the magnetic coupling value by other means, which will be subject Although the essence of the method of operation is similar to the ntrol applied in the hard switching converters, here the (resonant voltage) must be calculated in advance (each half-period)

The Efficient and Stable Charging of Electric Vehicle Batteries 373 voltage, i.e. the . The output , as the measured value from the electronic load shows in Fig. 17.

Output voltage and power shown by electronic load

The transformer ratio (if it were ideally coupled transformer) should correspond to the ondary winding turns, i.e. to be then he transformer here is not ideal. The maximum coupling is obtained by maximal approximation between the primary and secondary ferrite plates ideal magnetic coupling transforms the Following (3), mode" control called "instantaneous" control of the the internal processes of the SLSR converter to LSR converter shows in In case of the magnetic coupling observed and included into calculation. It is also possible to obtain the magnetic coupling value by other means, which will be subject method of operation is similar to the n the hard switching converters, here the internal period) and used for "predictive" regulator of power. As it is shown in [START_REF] Valtchev | Efficient Resonant Power Conversion[END_REF] the resonant capacitor voltage is a reliable measure for the (rectified) average resonant current. The circuit considered above and shown in Fig. 5, is unidirectional, i.e. no flow of energy is possible from the output to the input. Thus, being incapable to reduce rapidly the charge of the output (filter) capacitor by reversing the flow of energy, the shown above versions (Fig. 5 and all that follow it) are not so fast in reducing the output voltage. The output voltage is only reduced by the consumption of the load. This is not a problem because even this method of regulation has sufficiently faster reaction (at least equal to the classic CM control). In case of additional controllability of the secondary (receiver) side, this method of control will be even faster.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Discharge Depth (80% and 50%) until 20% change in power and internal resistance

Fig. 3 .

 3 Fig. 3. Charging process curves of 40 Ah, SE40AHA battery (Sky Energy, China)

Fig. 4 .

 4 Fig. 4. Street bus with dynamic charging by underground transmitters (KAIST, Korea)

Fig. 5 .Fig. 6 .

 56 Fig. 5. DC-DC series resonant converter with transformer connection to the output rectifier Fig. 6. Equivalent circuit of the simplified SLSR power converter

Fig. 7 .Fig. 8 .

 78 Fig. 7. Resonant current i r intervals and power switch commandFig. 8. Super-resonant (FM) operation of SLSR converter Expression (2) involves the capacitor voltage v c (t 1 ) at which the conducting switch (or the diagonal) is switched off (t 1 corresponds to the angular point x k ). The equation involves also the previously measured amplitude v cmax1 and the necessary next amplitude v cmax2 which may be different if it would be necessary to change the energy portion.In the case of contactless power transfer, the converter operation is described by the same expression (2) where v cmax1 and v cmax2 correspond to the resonant capacitor voltage amplitudes in the primary[START_REF] Valtchev | Resonant Contactless Energy Transfer with Improved Efficiency[END_REF]. In that case, the normalized output voltage q in (2) will be substituted by the corrected value T

Fig. 10 .

 10 Fig. 10. Resonant converter power stage (from APRONEX Ltd.)

Fig. 14 .Fig. 15 .

 1415 Fig. 14. Secondary and primary windings respectively in yellow/green and blue Fig. 15. Comparison between the new solution and previously adopted geometries

Fig. 16 .

 16 Fig. 16. Measured waveforms of the resonant converter: Vc, i(L) and input (supply) voltage Vs The transformer ratio (if it were ideally coupled transformer) should correspond to the number of turns of the primary winding divided by the sec 9:1. It should be expected the normalized output voltage 907 . 0 115 6 , 11 9 = = V V x q .

Fig. 17 .

 17 Fig. 17. Output voltage and shown by electronic load

  *74V=64V. In fact the moment of switching off is at a capacitor voltage of 64

  *74V=64V. In fact the moment of switching off is at a capacitor voltage of 64