Luis Romba

Stanimir S Valtchev

Rui Melício
email: ruimelicio@gmail.com

Single-phase Wireless Power Transfer System Controlled by Magnetic Core Reactors at Transmitter and Receiver

Keywords: Wireless power transfer, strongly magnetic coupling, magnetic core reactor

The applications of wireless power transmission have become widely increasing over the last decade, mainly in the battery charging systems for electric vehicles. This paper focuses on the single-phase wireless power transfer prototype controlled by magnetic core reactors in either side of the system: that of the transmitter, and that of the receiver. The described wireless power transfer system prototype employs a strong magnetic coupling technology to improve the power transmission efficiency. In the same time, a magnetic core reactor is used to control the "tuning" between the transmitter and the receiver frequencies, allowing for that increase of the system efficiency. Finally, practical results of the implemented prototype are presented.

Introduction

The Wireless Power Transfer (WPT) is a system that allows the transfer of electric power from a point in space to another point in space without any physical support, i.e., without wires or galvanic contact [START_REF] Ke | Improvement of the coupling factor of Litz-wire coil pair with ferrite substrate for transcutaneous energy transfer system[END_REF]. For relatively short and midrange distances, i.e., from a few millimeters to some meters, the transmission is performed mainly by magnetic field. The WPT systems used at short distances have a wide range of purposes. The applications start from battery charging of domestic appliances, mobile phones and laptops, even of Implantable Medical Devices (IMD), e.g. pacemakers [START_REF] Park | Enhancement of wireless power transmission into biological tissues using a high surface impedance ground plane[END_REF][START_REF] Jolani | Design of wireless power transfer systems using magnetic resonance coupling for implantable medical devices[END_REF]. The performance of these devices is largely dependent on the high efficiency of the power transfer. The best way to optimize the power transfer efficiency is to increase the (magnetic) coupling between the two coils. This is one of the rules of thumb that should be applied to the WPT systems [START_REF] Benjamin | Magnetic resonant coupling as a potential means for wireless power transfer to multiple small receivers[END_REF].

Due to the increased demand for the green energy systems, the WPT systems for midrange distances have had a significant development in the research related to battery charging applications for hybrid and electric vehicles. Hence, in the practice the midrange battery charger uses the strongly coupled magnetic resonance technology [START_REF] Qiu | Overview of wireless charging technologies for electric vehicles[END_REF]. The strongly coupled magnetic resonance technology is characterized by two pairs of control loops, one for the transmitter the other for the receiver (Fig. 1).

The internal impedance of the Source Generator can be annotated later as s R , and the impedance of the Load can be marked as further on as l R . Those two impedances (real, at the resonant frequency) are excluded further on, from the

C L R
resonators, leaving the resonator circuits independent of the generator and the load.

The equivalent circuit of the WPT using strongly coupled magnetic resonance technology is shown in Fig. 1.

AC

Lt Lr

Ct Cr

Receiver drive coil Transmitter drive coil

Transmitter resonant circuit

Receiver resonant circuit

Load Source generator

Ltd Lrd

Fig. 1. Simplified equivalent circuit of the WPT system.

The transmission efficiency, when a strongly coupled magnetic resonance technology is used, drops down with any directional misalignment. The maximum efficiency occurs when a correct impedance matching between the source and load will be achieved [START_REF] Austrin | Modeling of a three-phase application of a magnetic amplifier[END_REF].

The impedance adjustment can be obtained in two ways. One way is by tuning the operating frequency, and the other is by keeping a fixed operating frequency but tuning the impedance matching between the transmitter and the receiver systems [START_REF] Austrin | Modeling of a three-phase application of a magnetic amplifier[END_REF][START_REF] Trinkaus | Magnetic amplifiers another lost technology[END_REF]. The described WPT system includes two magnetic core reactors (MCR), one on each side of the system, i.e., the transmitter and the receiver, as shown in Fig. 2.

AC

Lt

Single-phase Wireless Power Transfer System 415

The MCR is in fact, a part of the well-known in the past "magnetic amplifier" [START_REF] Xu | Integrated V2G, G2V, and renewable energy sources coordination over a converged fiber-wireless broadband access network[END_REF]. It is formed by two circuits, one is the control or primary circuit where a DC current is applied for controlling the secondary circuit, where the circulation of AC current is regulated [START_REF] Xu | Integrated V2G, G2V, and renewable energy sources coordination over a converged fiber-wireless broadband access network[END_REF]. When the control current (DC) changes, it causes a variation of the magnetic flux. This change in the magnetic flux produces a variation of the magnetic permeability of the circuit. If the circuit is working in the linear part of the curve of the hysteresis cycle, the variation of the magnetic flux is approximately constant [START_REF] Fabbri | Impact of V2G/G2V technologies on distributed generation systems[END_REF]. However, if the operating point is above the knee of the curve of the hysteresis loop, the variation is always much more significant and the control is more efficient. This enables the frequency adjustment to be carried out either at the transmitter or the receiver of the WPT system, by changing the value of the inductance connected in series with the corresponding resonant coil, causing a change in the total circuit inductance [START_REF] Xu | Integrated V2G, G2V, and renewable energy sources coordination over a converged fiber-wireless broadband access network[END_REF][START_REF] Fabbri | Impact of V2G/G2V technologies on distributed generation systems[END_REF].

Relationship to Smart Systems

In contemporary societies the combination of nanoelectronics, embedded systems, cybernetics and intelligent systems, has and will continue to have a fundamental importance. That combination (smart system) is present in almost all modern products and services [START_REF] Thales | ARTEMIS-IA Pre-Brokerage event[END_REF], being the decisive set of elements and key enabling technologies in many innovations and solutions for societal challenges, being also a very significant factor in the competitiveness, employment and prosperity of countries and regions [START_REF] Thales | ARTEMIS-IA Pre-Brokerage event[END_REF].

The smart systems combine cognitive functions with sensing, actuation, data communication and energy management in an integrated way. The enabling principles of this combination are now the nanoelectronics, micro-electro-mechanics, magnetism, photonics, chemistry and radiation [START_REF] Neul | Smart Systems Integration in the Context of ECSEL[END_REF]. The smart systems are integrated with the (natural, built and social) environment, networks for power and data, other smart systems and the human beings [START_REF] Neul | Smart Systems Integration in the Context of ECSEL[END_REF].

The mobility sector is facing major challenges, e.g. the CO2 emissions reduction, together with the implied improvement of air quality especially in large cities and the reduction of traffic congestion still using the existing infrastructure wherever possible.

The development is made possible by electronics, with new components and systems, as well as by introduction of new methods and tools. It has come to support the concepts that will provide new vehicles, transport systems and infrastructures with intelligence and flexibility to meet the challenges of industry in this sector [START_REF]Multi Annual Strategic Research and Innovation Agenda for ECSEL Joint Undertaking[END_REF]. The innovation, development and research related to the smart mobility has its emphasis on the capabilities in the areas of detection, communication, geo-positioning, computing and decision making and control based on Electronics, Components and Systems (ECS) [START_REF]Multi Annual Strategic Research and Innovation Agenda for ECSEL Joint Undertaking[END_REF]. ECS functions will lead to resource-efficient transportation as they enable partly or fully electrified as well as advanced conventional vehicles that are clean, CO2-optimized and smartly connected to renewable energy sources [START_REF]Multi Annual Strategic Research and Innovation Agenda for ECSEL Joint Undertaking[END_REF].

Two examples of the development of specific applications in smart mobility are: smart systems for electro-mobility and smart systems for automated driving. In the first are included electric drive train, battery and battery management and charging technologies and the second is the included automated driving without the driver in the loop, on highways and in urban regions, but allowing the driver to intervene [START_REF] Neul | Smart Systems Integration in the Context of ECSEL[END_REF].

Modelling

The magnetic core circuit of a WPT system, when a correct alignment is achieved, is shown in Fig. 3. The coils are wound on the middle leg of each "E" part of the cores, upper "E" part corresponding to the primary, and the bottom part to the secondary. According to the structure of the magnetic core and the dimension of the air gap, the reluctance values are indicated as follows:

for the primary branches, and are the secondary parts of the magnetic core reluctance. The , are the reluctance elements that correspond to the air gap, and are the magnetic leakage reluctance values of the primary and the secondary. The simplified equivalent magnetic circuit is shown in Fig. 4, where seven loops or sub-circuits are marked.

Rag1

Rag 2 Rag 3 Based on this circuit (Fig. 3), the equation of the total reluctance can be deduced as: The mutual inductance, is given by:

R 4 R 5 R 2 R 6 R 7 R 8 R10 R 1 R 3 R 9 R l1 R l2 R l3 R l4
(2)

The result of the replacing of the (1) in ( 2) is given by:

(3)

The coupling coefficient is given by: The transmitter coils set, the receiver coils set and the two magnetic core reactors, one for each circuit [START_REF] Romba | Improving magnetic coupling for battery charging through 3D magnetic flux[END_REF][START_REF] Romba | Three-phase magnetic field system for wireless energy transfer[END_REF] are shown in Fig. 7. The tests were conducted step by step. In the first step a frequency of the function generator was selected (Fig. 2). This choice is a hardware simulation of the reality where the transmitter frequency is defined as the transmitter control has decided. The second step was to tune the transmitter resonant circuit by applying DC current to the transmitter magnetic core reactor until obtaining the maximum voltage that is observed at the resonant circuit. This step corresponds to the set point optimization. The third step was to tune the receiver resonant circuit following an identical procedure to the previous step, i.e. obtaining the maximum voltage at the receiver resonant circuit. During those procedures, a constant load value was maintained. The values of the total inductance for the transmitter and for the receiver are shown in Fig. 8.

The resonant frequency values for each inductance value, maintaining constant the capacitance, are shown in Fig. 9.

The measured voltages by the oscilloscope (Fig. 10) show the input voltage (channel 1), and the voltage value in the transmitter resonant circuit (channel 2). The channel 3 is the voltage value measured at the receiver resonant circuit. The channel 4 is the voltage value applied to the load.

Fig. 2 .

 2 Fig. 2. WPT with magnetic core reactor in both sides.

Fig. 3 .

 3 Fig. 3. Simplified equivalent magnetic circuit.

Fig. 5 .

 5 Fig. 5. Variation of total magnetic reluctance function of the air gap.

  The global physical model presented in this paper was performed in Power Electronics Laboratory, Faculty of Science and Technology, New University of Lisbon and is shown in Fig.6. The implemented schematic diagram is based on Fig.2.

Fig. 6 .

 6 Fig. 6. Global physical model implemented.

Fig. 7 .

 7 Fig. 7. Partial view implemented circuit with the two MCRs.

Fig. 8 .

 8 Fig. 8. The total Inductance values to the transmitter and receiver.

Fig. 9 .

 9 Fig. 9. The resonance values to the transmitter and the receiver.

(1)

The result is shown in Fig. 4.

Conclusions

In this paper a different approach is presented, applying the magnetic reluctance elements to determine the mutual inductance and the coupling coefficient. This process makes possible to obtain, with reasonable precision, the most important values in order to design a project of a wireless power transfer through a simple distances measurement. In the case study, it is shown a WPT implemented physical model in which a frequency control is presented using two magnetic core reactors. Through the curves shown in Fig. 8 and in Fig. 9, it is possible to predict the DC current that will be necessary for controlling the magnetic core reactors, maintaining constant the resonant capacitor values, and in the same time, to tune the transmitter and the receiver at the same frequency. The stronger magnetic coupling, as presented in the circuits, shown in the Fig. 6 and in Fig. 7, allows to obtain a sine wave at the output even if the input signal is not a pure sinusoidal wave. As it is shown in Fig. 10, it is made possible by the two resonant circuits mounted at each side: the transmitter (distorted, channel1) and the receiver (channel 4).


Martim V Carvalho

António J Arsenio

Carlos Cardeira

Paulo J Costa Branco

Rui Melício
email: ruimelicio@gmail.com

Levitating Bearings using Superconductor Technology Under Smart Systems Scope

Keywords: Superconductor magnetic bearing, superconductors, Smart Grids, Magnetic levitation, Zero field cooling

This paper presents a study about the cooling and leakage aspects of superconductor magnetic bearings towards their integration on a smart systems scope to keep the stability of the cooling system. The use of superconductor magnetic bearings in electric power generation, namely in wind power has been increased due to their weight, friction less, and volume reduction advantages. The novelty of this paper is the use of Zero-Field Cooling instead of the common Field Cooling technique. On the other hand, this new kind of bearings needs a constant flow of liquid nitrogen to keep the superconductivity properties. A prototype was modelled, simulated and implemented for experimental validation.

Introduction

Technological Innovation for Smart Systems is essential for smart grids. Wind power systems manifest variations in the output power due to the variations wind speeds, thus introducing a new factor of uncertainty and risk on the electrical grid posing challenges in terms of power system security, power system stability, power quality and malfunctions. The significant breakthroughs in science and technology allow the opportunity to link cyber space and physical components, using technological Innovation for Smart Systems. This bridge leads to Cyber-Physical Systems [START_REF] Viveiros | Control and supervision of wind energy conversion systems[END_REF].

Large wind power generators with superconductors bearings are in constant development due to their advantages such as weight, friction less, volume reduction and the increased efficiency when compared with conventional bearings technologies [START_REF] Seixas | Simulation of offshore wind system with three-level converters: hvdc power transmission in cloud scope[END_REF][START_REF] Arsénio | Conception of a YBCO superconducting zfc-magnetic bearing virtual prototype[END_REF]. The use of Superconductor Magnetic Bearings (SMB) requires their constant cooling with liquid nitrogen. Smart grids allow the optimization of the available resources, namely in keeping and monitoring the liquid nitrogen critical levels.

Classical rotating bearings are a frequent component of rotational systems. They are widely used in wind energy production, hydraulic power plants and thermo power plants. The SMBs studied in this work will likely replace the existing classical bearings in a near future. Despite the SMB increased efficiency and life time as well as the frictionless advantages, they have other drawbacks. Actually they need a cryogenic system to maintain the working low temperatures that are needed (by now) to keep the superconductivity properties.

Advanced researches have been carried out to build SMBs. Prototypes have been developed for many applications like the construction of large-scale flywheels [START_REF] Ogata | Test equipment for a flywheel energy storage system using a magnetic bearing composed of superconducting coils and superconducting bulks[END_REF] or for bearings in the textile industries [START_REF] Sparing | Superconducting magnetic bearing as twist element in textile machines[END_REF], or for wind energy conversion systems [START_REF] Lloberas | A review of high temperature superconductors for offshore wind power synchronous generators[END_REF].

The main contribution of this paper is the use of the zero field cooling (ZFC) technique instead of the classical field cooling (FC) technique. The approach in this work uses has shown to be more efficient, presenting less Joule losses [START_REF] Da Costa Branco | Design and experiment of a new maglev design using zero-field-cooled YBCO superconductors[END_REF][START_REF] Fernandes | Superconductor joule losses in the zero-field-cooled maglev vehicle[END_REF]. Moreover the Nitrogen consumption is analyzed as it is an important feature of SMB, regarding the economic impacts of its use.

Relationship to Smart Systems

A smart grid is a new type of power grid which highly integrates modern advanced information techniques, communication techniques, computer science and techniques with physical grids [START_REF] Zhang | New challenges to power system planning and operation of smart grid development in China[END_REF]. One of the objectives of smart grid is the optimization of resources allocation.

Cyber Physical Systems (CPS) is an important issue for smart grids and smart sensors. The coordination and interaction of cyber layers and physical components of wind systems require handling a number of challenges for essential feasibility and competiveness of future. Successfully integrating wind power into the existing electric power grid is a complex challenge that relies on distributed, interconnected cyber-physical systems, which are proliferating within the engineering industry [START_REF] Viveiros | Control and supervision of wind energy conversion systems[END_REF].

One of the objectives of smart grid is the optimization of resources allocation. The use of SMBs require a critical allocation of smart systems, namely smart sensors to support the continuous availability of liquid nitrogen or to measure guidance and levitation forces, inside the SMB. These smart sensors can transfer information to a cloud architecture system, benefiting of existing Internet Cloud Services [START_REF] Seixas | Simulation of offshore wind system with three-level converters: hvdc power transmission in cloud scope[END_REF].

SMB Cooling Prototype

An SMB based on NdFeB permanent magnets and YBCO superconductor bulks, also referred to as high temperature superconductors was designed. The SMB viability was made in [START_REF] Arsénio | Viability of a frictionless bearing with permanent magnets and HTS bulks[END_REF][START_REF] Arsénio | Experimental set-up and efficiency evaluation of zero-field-cooled (ZFC) YBCO magnetic bearings[END_REF] and the conception and experimental evaluation the SMB was achieved in [START_REF] Arsénio | Conception of a YBCO superconducting zfc-magnetic bearing virtual prototype[END_REF], as shown in Fig. 1.

In [START_REF] Arsénio | Viability of a frictionless bearing with permanent magnets and HTS bulks[END_REF] the magnetic fields as well as the levitation and guidance forces were simulated in a finite element analysis tool.

The levitation forces, as shown in [START_REF] Arsénio | Viability of a frictionless bearing with permanent magnets and HTS bulks[END_REF] are given by:

2 2 t Lev B A F   (1)
where A is the surface parallel to the xy plan, Bt is the magnetic flux density tangential component and µ is the magnetic permeability of the YBCO superconductor bulks.

The SMB was simulated in a finite element analysis tool, as shown in Fig 1 . The arrows show the levitation forces FLev which were shown to be able to sustain the rotor weight [START_REF] Arsénio | Viability of a frictionless bearing with permanent magnets and HTS bulks[END_REF]. The geometric placement of the permanent magnets and high temperature superconductors is carefully performed to keep symmetry along the main axis and to minimize the air gap of the prototype between the rotating part (rotor) and the static part (stator). Moreover, studies made during the development of this work involve changes in these geometric placements, either in the rotor as in the stator. Additionally, more finite element modelling was designed for simulation and viability study of the bearing, calculating the estimated levitation and guidance forces involved. The designed prototype model is shown in Fig. 2. Experimental validation was achieved by building a structure in conformity with the previously simulated geometry and comparing the simulation results with the ones obtained by measuring the existing forces in the real prototype. The results allowed the conclusion that it is possible to build a superconductor magnetic bearing using the zero field cooling technique, providing an important insight on how the system behaves.

To keep the YBCO superconductor bulks cooled, channels were created inside the stator for Liquid nitrogen cooling, as shown in Fig. 3. As the results were conclusive, a real prototype was implemented. Polyethylene and PLA were the materials used for the stator and rotor. To confirm the choice of the materials, namely the stator and the fasteners parts, an experience was developed to study the behavior of the structure at working conditions. These conditions are characterized by working at low temperatures, in the order of 77 K (liquid nitrogen temperature) and under clamping forces that close the stator slices together. With the intention of not harming all the structure, only half of the stator part was used in these first tests. Hence, the half of the stator part was totally submerged in liquid nitrogen, without any HTS bulks inside, until the system achieved a state of stability. For this purpose, some cables were attached to the structure so that it could be pulled. An example of this process is shown in Fig. 4.

 Submerged tests

In this first test, the used materials presented a good resistance within the working temperature range during the first experiences, without breaking or exhibiting any type of weakness.

 Leak tests

To ensure the structure, namely the stator, would enclose the liquid nitrogen in an efficient way, some leak tests were elaborated. These tests were made in a first phase with water, until the stator was successfully insulated.

In the first attempt, the stator was clamped only by the four holes in each corner. After pouring some water into the stator through the nitrogen entrance channel, it was observed that the water would come out through breaches between the slices of the stator. In order to correct this issue, a rubber based insulation tape was applied between each slice of the stator in order to insulate the structure. This insulation tape was cut accordingly to the profile of the inner part of the stator slices, so that it was applied only where the slices make contact. It was observed that some water still fell between the breaches, although in much less quantity. Subsequently it was decided to provide a better distributed and more uniform clamping to the structure, closer to the inner part of the stator. In a first phase, this was achieved by using clamps with PVC plaques to distribute the load. An example of how the structure was clamped is shown in Fig. 5. As this solution proved to solve the tightness problem, it led to a change in the design of the structure explained, where as a way to avoid the clamps, 8 holes of 6.5 mm were designed closer to the inner part of the stator to distribute the loads uniformly.

 Nitrogen pouring

Liquid nitrogen is unstable when exposed to room temperature. Since its boiling point is at 77 K, it immediately evaporates. For this reason, when it is poured into the stator it often starts to boil, spilling if not handled with caution. Hence, the pouring of nitrogen is executed slowly in short movements, periodically switching channels of nitrogen entrances. This process usually takes about 10 min to 15 min, until the structure is full of stabilized liquid nitrogen. At this point we know that the temperature within the stator bulks is 77 K.

To complete the leak test, liquid nitrogen was poured inside the stator and the results were satisfactory, as the stator remained well insulated without any nitrogen spills.

 Nitrogen usage

In order to estimate the rate at which the liquid nitrogen would evaporate from inside the stator, a graph of time vs. weight was elaborated. This was achieved by pouring liquid nitrogen inside the stator until the structure was full. To read the weight values a weighting scale was used. With the structure standing on the weighing-scale, the time was measured until the weight stopped falling. With the purpose of not doing any damage to the weighing scale with the liquid nitrogen, a box of Styrofoam was used to protect the scale. The proper tare of this box was made to allow the precise reading of the structure weight. The set-up established to read the weigh values is shown in Fig. 6. Fig. 6. Set-up used for the nitrogen usage information.

The curve of weight vs. time is shown in Fig. 7. It is easy to see that the rate at which the liquid nitrogen evaporates in the first 600 seconds can be considered linear, and therefore possible to calculate. In the calculation of the nitrogen usage rate, the first value considered was the weight measured after one minute, because after pouring the liquid nitrogen in the stator, it takes some seconds to stabilize. The evaporation rate in g/min of the liquid nitrogen in the stator was calculated and is given by: LNer=(814-727)/(600-60)=0.161 g/s =9.67 g/min Knowing that the density value of LN2 is 0.807 g/mL, the evaporation rate in mL/min is given by: LNer =0.161/0.807=0.200 mL/s =11.979 mL/min Fig. 7. Superconductor bearing consumption of LN2.

Conclusions

One of the objectives of smart grid is the optimization of resources allocation. As shown in this paper, the Superconductor Magnetic Bearings usage requires a critical allocation of smart systems, namely smart sensors to support the continuous availability of liquid nitrogen. The SMB viability was shown, a prototype was build and the LN2 consumption was measured. This data is important for smart grid management, and requires a comprehensive smart system to support the continuous availability of liquid nitrogen or to measure and guidance and levitation forces inside the Superconductor Magnetic Bearing. Further work will deal with the integration of this data into smart systems, in an embedded architecture which embodies advanced automation systems to provide control and monitoring over the continuous availability of liquid nitrogen or to measure and guidance and levitation forces inside the Superconductor Magnetic Bearing.

Fig. 1 .

 1 Fig. 1. SMB simulation of magnetization directions and contours.

Fig. 2 .

 2 Fig. 2. Prototype model.

Fig. 3 .

 3 Fig. 3. Prototype model: channels for LN2 cooling.

Fig. 4 .

 4 Fig. 4. Totally submerged stator in LN2.

Fig. 5 .

 5 Fig. 5. Clamps with PVC plaques.

Acknowledgments. This work was supported by Fundação para a Ciência e a Tecnologia, through IDMEC, under LAETA, project UID/EMS/50022/2013 and under project PTDC/EEEI-EEL/4693/2014 -HTSISTELEC. The authors would like to thank Mitera and Fablab EDP for their help and availability to do the complex machinery involved in this work.


Isaías L R Gomes

Hugo M I Pousinho

Rui Melício
email: ruimelicio@gmail.com

Vitor M F Mendes

Stochastic Optimization for the Daily Joint Operation of Wind/PV and Energy Storage

Keywords: Day-ahead electricity market, energy storage, mixed-integer linear programming, stochastic optimization, wind with PV powers

This paper deals with the problem of optimal bidding in a day-ahead market of electricity for a power producer having joint operation of wind with photovoltaic power systems and storage of energy. Uncertainty, not only on electricity market prices, but also on wind and photovoltaic powers, has to be faced in order to achieve optimal bidding. The problem is viewed as a sort of a two-stage stochastic optimization problem formulated by mix-integer linear programming. A case study with data from the Iberian Peninsula is presented and a comparison between joint and disjoint operations is discussed, allowing concluding that the joint operation attenuates the economic impact of disjoint operation volatility.

Introduction

Although of the uncertainty on the availability of power, renewable energy sources (RES) exploitations are the main announced measures to reduce anthropogenic greenhouse gases emissions and the way of future in what regards a sustainable development [START_REF] Gutierrez | Effects of wind intermittency on reduction of CO2 emissions: the case of the Spanish power system[END_REF]. The exploitation of RES has worldwide grown rapidly, benefiting from sustenance schemes and incentive policies [START_REF] Wang | A review on promoting share of renewable energy by greentrading mechanisms in power system[END_REF]. A main characteristic of the RES exploitation of wind power (WP) and of photovoltaic power (PVP) is uncertainty, which have to be considered in the scheduling of these powers. WP or PVP systems standing alone are not capable of warranting power generation during all day due to the uncertainty in the availability of the source of energy at different time periods of day or different periods of the year. The mismatch between periods of low availability of these sources lead to a question about if complementarity between WP and PVP has a better appropriateness for presenting a single offer in a day-ahead market (DAM). Also, energy storage is a way to harness RES, allowing the store and discharge of energy at adequate conditions. A report of the NREL states that the challenges associated with meeting the variation in demand while providing reliable services has motivated historical development of the storage of energy and that large penetrations of variable generation rises the need for flexibility options [START_REF] Denholm | The role of energy storage with renewable electricity generation[END_REF]. Large scale RES integration without energy storage is said to be a challenge for future power systems [START_REF] Beaudin | Energy storage for mitigating the variability of renewable electricity sources: an updated review[END_REF]. Hence, storage technologies play an important role in the actual deregulated markets, providing capability for arbitrage, increasing the economic value of RES [START_REF] Divya | Battery energy storage technology for power systems-an overview[END_REF]. Joint operation with the aim of submitting in a DAM a single bid of a producer having WP, PVP and a battery for energy storage is the line of research perused in this paper. Which has as a contribution the application of MILP approaches to two-stage stochastic optimization problems for optimal bid of: WP; PVP and joint operation of WP, PVP and energy storage device.

Relationship to Smart Systems

Intelligence in the form of algorithms, new computing models, and advanced analytics prone to allows enhancement in decision making [START_REF] Gomes | Bidding and optimization strategies for wind-pv systems in electricity markets assisted by CPS[END_REF]. Power system can benefit from this intelligence. A smart system can be stated as an embedded system that incorporates advanced systems and provide the inhabitants with sophisticated monitoring and control over how something happens in the system [START_REF] Arsan | Smart systems: from design to implementation of embedded smart systems[END_REF], for example, the management of a wind system, a PV system or a storage device. Smart systems are capable of sensing, making diagnosis, describing, qualifying and managing how something happens in the system, incorporating both technical intelligence and cognitive functions. This incorporation is labeled as the third-generation of smart systems, offering an interface between the virtual and the physical world. Smart systems are highly reliable, often miniaturized, networked, predictive and energy autonomous [START_REF] Topham | Smart systems integration knowledge gateway[END_REF]. The exponential increase in energy demand, that is forecasted to be around 45% by 2030, and the increase integration of RES in power systems are facts driving research and development about managing how things happen in the system [START_REF] Glesner | Embedded systems design for smart system integration[END_REF]. Particularly, energy management will be one of the most urgent topics of the century and a significant driver for research and developments [START_REF] Glesner | Embedded systems design for smart system integration[END_REF]. Important issues in energy management are reliability and efficiency. These issues are implied by the change of paradigm from the traditional electric grid to a smart grid one [START_REF] Glesner | Embedded systems design for smart system integration[END_REF]. A smart grid in power system can lead to better results in decision-making under uncertainty. Particularly, monitoring and high quality real-time data for the exploitation of WP or PVP are a valuable contribution to mitigate the implications of uncertainty, mitigating the loss of a producer's profit due to bad decisions of bidding in a day-ahead electricity market. So, smart systems in an electric grid are expected to be: crucial for increase of power reliability; favorable for achievement of better bidding in electricity markets and an overall enhancement in power system.

State of the Art

One of the greatest challenges for exploitation of WP or PVP is to face the intermittence and variability of those powers in order to mitigate the uncertainty in power generation. Exploitation of WP or PVP by a power producer bidding in DAM at day d-1 has a menace. Which is due to the power output delivered at day d being predisposed to differ from the clearing accepted level of power in the market.

Consequently, the power producer enters in imbalance of delivering and is subjected to economic penalty. Therefore, uncertainty in power output has to be properly accommodated into the decision of bidding. A complementarity of WP with PVP has been shown on the Iberian Peninsula, leading to a question about if the joint exploitation of the operation of WP with PVP is able to mitigate the uncertainty in the power output [START_REF] Jerez | Spatio-temporal complementarity between solar and wind power in Iberian Peninsula[END_REF]. Literature dealing with WP producer (WPP) mitigation of WP uncertainty favors the implementation of combining strategy with the aim of reducing uncertainty on power generation. This combining strategy for a WPP includes the use of energy storage technology, namely pump-storage plants, compressed air facilities or vanadium redox flow batteries [START_REF] González-Garcia | Stochastic joint optimization of wind generation and pumped-storage units in an electricity market[END_REF][START_REF] Angarita | Combined hydro-wind generation bids in a pool-based electricity market[END_REF][START_REF] Sundararagavan | Evaluating energy storage technologies for wind power integration[END_REF]. Also, the use of: purchase call/put options to pumped-storage facility is proposed for wind producers to hedge against WP uncertainty [START_REF] Hedman | Comparing hedging methods for wind power: using pumped storage hydro units vs. options purchasing[END_REF]; joint operation of WP, PVP, pump-storage power plant and energy storage devices is proposed for reserve market [START_REF] Parastegari | Joint operation of a wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets[END_REF].

Stochastic nonlinear programming is an approach suitable for the bidding strategy of a WPP to achieve convenient decision with the aim of considering uncertainty [START_REF] Matevosyan | Minimization of imbalance cost trading wind power on the shortterm power market[END_REF]. Even more suitable, if an approach supported by MILP is conceivable. MILP either deterministic [START_REF] Pousinho | Investigation on the development of bidding strategies for a wind farm owner[END_REF] or stochastic [START_REF] Laia | Self-scheduling and bidding strategies of thermal units with stochastic emission constraints[END_REF] have been proposed to convenient address the appropriated decision of bidding strategy a WPP needing to consider uncertainty.

Profit Description

A power producer exploiting WP or PVP needs to consider the uncertainty on the availability of power impacts in the worth of bidding in a DAM. If joint operation of the exploitation of these powers with energy storage is possible is important to further research the worth of profiting from the mismatch between periods of low availability of these powers. This research is the subject of the following problem description, having the objective of enquiring if this joint operation can improve profit of the power producer in DAM. A prerequisite is that the market rules permit that joint operation participates in the aforementioned electricity market. The concept presented in this paper is the same of the concept of a Virtual Power Plant (VPP). A VPP is defined as a power plant that aggregates different distributed generation units, such as WP and PVP systems, in order to participate in the market as a single entity. This participation is expected to not only facilitate trading of energy in electricity markets, but also being prone to achieve better profits.

The power system balance between the total amount of energy converted into electric energy and the usage of electric energy has to be null in real time. This balance has to be guaranteed with a convenient anticipation relatively to the real time delivering. Usually, the balance is guaranteed by production/consumption programs established in advance that guarantees system balance and by a real-time balancing market. The producer imbalance or the system imbalance may be negative, null or positive in the market, but as long as there is producer imbalance the producer is subjected to a market penalty procedure. The procedure in the Iberian electricity market is to subject the producer to a price for the positive energy imbalance and another price for negative energy imbalance. In the Iberian electricity market the procedures for computing these prices are reported in [START_REF] Gomes | Optimal wind bidding strategies in day-ahead markets[END_REF]. The imbalance incurred by a power producer with a power plant k in a disjoint assessment in time t is stated as follows:

k k k t ts t d P P  . (1)
In ( 1) k ts P is the physical delivering of energy in time t and k t P is the bid submitted in the DAM. An imbalanced of a physical energy delivering is associated with a price ratio: positive and negative imbalances are associated with price ratios t pr  and t pr  , respectively. The positive price ratio is stated as follows:

,1 t tt t pr pr      . ( 2
)
The negative price ratio is defined as follows:

,1

t tt t pr pr      . (3)
In ( 2) and (3) t  is the DAM price and t   and t   are respectively the positive and negative imbalance prices. Consequently, the power plant k at period t in a disjoint assessment has a profit given as follows:

() k k k k t t t t t t t t t PR P pr d pr d           . (4)
In (4) the first term represents the profit associated with the DAM price t  from an accepted production k t P . The term in brackets is associated with the income derived from the procedure of economic penalty. The positive and negative deviations are respectively quantified by k t d  and k t d  subjected to the constraint of at least one of the deviations is always null at a period t.

Problem Formulation

The goal of optimization problem is the profit maximization. Maximization of profit implies a convenient management of the economic impact of energy delivering deviations. This management has to face uncertainty on market prices and on availability of energy in due time to be converted into electric energy in order to satisfy delivering. If there is a mismatch between the energy traded and the physical delivering, then eventual losses on profit can occur. The uncertainties on WP and on PVP availabilities are modelled by a convenient selection of scenarios from a set S said to be the set of appropriate scenarios for the next day. A scenario s has the probability s  . The MILP formulation for the disjoint operation of WP or PVP systems are equivalent problems stated as follows: max  

,,

k k k ts ts ts d d d t s      (8) 0 , , k k k ts ts ts d P u t s      (9) max 0 (1 ), ,
k k k ts ts d P u t s       . (10)
In ( 6) the limit on the bid is set to be the rated power of the k unit (wind PV system). In [START_REF] Arsan | Smart systems: from design to implementation of embedded smart systems[END_REF] to [START_REF] Jerez | Spatio-temporal complementarity between solar and wind power in Iberian Peninsula[END_REF]

In [START_REF] Laia | Self-scheduling and bidding strategies of thermal units with stochastic emission constraints[END_REF] is the equation for the model of a Vanadium redox flow battery, assuming a null depth of discharge and considering the efficiency of charging and of discharging. This type of energy storage is one of the most promising technology for energy management in order to alleviate the variation and intermittence of WP or PVP.

The procedure of the joint bid strategy is shown in Fig. 1. In Fig. 1 the upper blocks are for scenario generation procedures obtained via historical data of wind speed, solar irradiance and historical data of the Iberian electricity market. After the blocks for scenario generation procedures, technical data blocks access the wind system, PV system capacities and energy storage speciation to

Wind power conversion

Scenarios

The energy traded and the absolute value of the energy deviation by the disjoint and by the joint operations are shown in Fig. 4. In Fig. 4, absolute energy deviation is the sum of the positive with the negative imbalances. Energy is stored at 5 h and delivered at the favorable price hour 21 h. The expected profits for the disjoint and joint with the energy storage operations are 98 308.96 € and 98 608.98 €, respectively. Hence, an increase of 300 €/day is expected with this joint operation. A higher increase occurs at 15 h, a period of favorable prices, low negative imbalance price ratios and high positive imbalance price ratios. Although, the numeric values change in function of availability of WP and PVP, the energy deviation is conveniently managed in what regards economic penalties in order to achieve better profit, i.e., this joint operation is never worse than the disjoint one.

Conclusion

In this paper, an optimization problem of bidding in DAM for a power producer having joint operation of WP with PVP and energy storage of the type Vanadium redox flow battery is presented. The problem is stated as sort of a two-stage stochastic optimization problem addressed as MILP problem. The joint bidding is envisaged as a favorable one when the mismatch of uncertainty due to the WP and the PVP is partial disabled by one another and an energy storage device allows flexibility of storing energy and discharging at hours of convenient DAM prices. A future work can include the participation in intraday-markets.

Fig 1 .

 1 Fig 1. Flowchart of joint bidding strategy.

Fig 4 .

 4 Fig 4. Left: energy traded. Right: absolute energy deviation.

  the imbalance is decomposed into a difference of positive values, i.e., the difference between the positive k   is added. The problem for joint operation of WP, PVP and energy storage is formulated as a sort of a two-stage stochastic optimization problem where the hourly bids and imbalances are first and second-stage variables, respectively. The goal of the problem is to find a single optimal bid in DAM for the wind power, PVP generation and energy storage. The stochastic MILP formulation is specified by the maximization problem stated as follows:

	bat t E		1 bat t E 				Chbat Chbat t P			1 Debat	t P	Debat	.	(18)
		b) Energy storage limits
	0	bat t EE 	bat	max	.	(19)
		c) Storage power limits
	0	Chbat  t P		t P	Chbat	max	t k	.	(20)
	0		P	Debat			P	Debat	max	(1		k	)	.
				t										t				t
																				d  and the negative k d  imbalances.
																				ts	ts
	If the imbalance is negative, the term		pr	k  is null and the term d		pr	k  is d
																				ts	ts	ts	ts	ts	ts
	subtracted. If the imbalance is positive the term		pr	k  is null and the term d
																				ts	ts	ts
								k
		pr			d
	ts		ts		ts
	max		ST 	   s	ts	t P	ts  ts ts pr d   		ts	ts pr   ts d		.	(11)
						11 st 
		General constraints
		a) Energy offer constraint
	0	max tt max W PV P P P P    	De	max	.	(12)
		b) Output power of combined wind power, PV power and energy storage device
	P		W P		P	PV		P	Chbat		P	Debat	.	(13)
	ts					ts						ts					t	t
		Imbalance constraints
	d		P  	P		,, t   s	(14)
		ts					ts				t
	d	ts			d	ts  ts d 	,, t   s	(15)
	0		d				P		u				, , t   s	(16)
				ts				ts		ts
	0	ts    d	(	P	PV			max		P	PV	max		P	De	max	)(1		ts u	), , t   . s	(17)
		Constraints of energy storage device
		a) Energy storage equation

Acknowledgments.

To thank the Millennium BCP Foundation for the financial support; and Foundation for Science and Technology-FCT: LAETA 2015/2020, ref: UID-EMS-50022-2013.

deliver the information into the block implementing the MILP approach for the twosage stochastic optimization method in GAMS/CPLEX.

Case Study

The case study is based on a wind-PV system in the Iberian Peninsula with rated power of 200 MW and an energy storage rated power of 30 MW. The day-ahead energy prices and the price ratios t pr  e t pr  are reported in [20]. The bidding is for a 24 h horizon on an hourly basis. The scenarios are 10 for WP, 10 for PVP, 10 for DAM prices and 10 for price ratios. The DAM-clearing price scenarios and the price ratios scenarios t pr  and t pr  are shown in Fig. 2. The scenarios for WP and for PVP are shown in Fig. 3.


Saber Talari
email: saber.talari@ubi.pt

Miadreza Shafie-Khah
email: miadreza@ubi.pt

Neda Hajibandeh
email: hajibandeh.n@ubi.pt

João P S Catalão
email: catalao@fe.up.pt

Assessment of Ancillary Service Demand Response and Time of Use in a Market-Based Power System Through a Stochastic Security Constrained Unit Commitment

Keywords: Ancillary service demand response, security constrained unit commitment, time of use, two-stage stochastic programming

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

Introduction

With the increasing of these renewable energy resources in the power systems, Independent System Operators (ISO) have been faced with new challenges, mainly related to the random and uncertain nature of wind speed [START_REF] Jannati | A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants[END_REF][START_REF] Zafirakis | Autonomous dual-mode CAES systems for maximum wind energy contribution in remote island networks[END_REF].

On the other hand, with growing the share of wind energy in power system's energy, novel methods have been proposed to improve the power consumption pattern of consumers [START_REF] Vos | Revision of reserve requirements following wind power integration in island power systems[END_REF]. Demand Response Programs (DRPs) are one of the most practical methods for this purpose.

With the expansion of smart meters, like Advanced Metering Infrastructure (AMI) and Internet of Things (IoT), in demand side of power systems, implementation of different methods of Demand Response (DR) is going to be much more applicable [START_REF] Arasteh | Iot-based smart cities: A survey[END_REF][START_REF] Kamyab | Demand response program in smart grid using supply function bidding mechanism[END_REF]. A suitable DR method can not only decrease total operation cost but also provide security and safety of the network operation [START_REF] Bahrami | From demand response in smart grid toward integrated demand response in smart energy hub[END_REF].

In [START_REF] Sioshansi | Evaluating the impacts of real-time pricing on the usage of wind generation[END_REF], an approach is proposed for shifting suitable amount of load from peak hours to off-peak hours to facilitate effective use of wind generators, reduce possible contingencies in the network and nonnegative local marginal prices (LMP).

Transferring and load shifting has been performed for those loads that can be moved from peak hours to off-peak hours under the load control of Independent System Operator (ISO), while the total load over the planning horizon is completely fixed. Load shifting of the consumers from peak hours to off-peak hours has been conducted by implementing Incentive Based Demand Response (IBDR) programs where only one of the incentive demand response programs has been modeled. By implementing several programs of demand response programs and using time-based demand response programs, more reliable results will be presented.

In [START_REF] Papavasiliou | Reserve requirements for wind power integration: A scenario based stochastic programming framework[END_REF], wind resources have been used for the flexibility of supply side and their uncertainties have been considered. A flexible stochastic security-constrained shedding framework has been presented for simultaneous optimization of utilization in the supply and demand side aided a demand response plan or program named Time of Use (TOU) optimization tariff [START_REF] Heydarian-Forushani | Robust scheduling of variable wind generation by coordination of bulk energy storages and demand response[END_REF]. In [START_REF] Liu | Quantifying spinning reserve in systems with significant wind power penetration[END_REF][START_REF] Pina | The impact of demand side management strategies in the penetration of renewable electricity[END_REF][START_REF] Heydarian-Forushani | Flexible Security-Constrained Scheduling of Wind Power Enabling Time of Use Pricing Scheme[END_REF], TOU tariffs have been optimized so that the flexibility potential of load side in confronting with the load uncertainty and large scale changes of generation side is maximized. In [START_REF] Critz | Power systems balancing with high penetration renewables: The potential of demand response in Hawaii[END_REF][START_REF] Dietrich | Demand response in an isolated system with high wind integration[END_REF], the impact of two important demand response strategies of Load curtailment and Load shifting on power systems with the wind generation has been investigated. In [START_REF] Zhechong | Impacts of high penetration wind generation and demand response on LMPs in day-ahead market[END_REF] the problem of constructing the bidding curve has been discussed for a retailer to offer in the pool market. The problem is formulated as a stochastic linear programming.

In this paper, the impacts of Ancillary Service DR (ASDR) and Time of Use (ToU) are studied. A day-ahead market with a balancing market is considered. With a Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of wind generation. The impacts of merging ASDR and ToU are thoroughly investigated.

Relationship to Smart Systems

The various current facilities in smart grids either in terms of communication technology e.g. Home Area Networks (HAN) or smart meters e.g. advanced metering infrastructures (AMI) enable end-users to participate actively in electricity market in order to improve security, economy, efficiency and reliability of network operation [START_REF] Boroojeni | Overview of the Security and Privacy Issues in Smart Grids[END_REF]. The information flow in the smart grids can be classified based on power flow and power system sections. Accordingly, communication architecture for demand response is introduced. Home Area Networks/Business Area Networks/Industrial Area Networks (HANs/BANs/IANs) are communication technologies which are deployed in residential units, commercial buildings, and industrial plants in order for connecting multiple electrical appliances to smart meter through ZigBee, WiFi, or Power-line communication (PLC) [START_REF] Sheikhi | A cloud computing framework on demand side management game in smart energy hubs[END_REF].

On the other hands, Neighborhood Area Networks/Field Area networks (NANs/FANs) are designed for communication between different smart meters of power distribution system through Data aggregate Unit (DAU), WiFi, world-wide interoperability for microwave access (WiMax) or cellular networks like GPRS, 3G and LTE. Meanwhile, for communication between bulk generation, transmission lines (Wide-Area Network) WANs is used through fiber-optic communication or microwave transmission.

Formulation

In this paper, two objective functions are introduced in order to deal with both pricedbased demand response that is ToU and incentive-based demand response which is ASDR. In fact, for the earlier, a two-stage stochastic program is run for maximizing social welfare and the later, a two stage-stochastic program is run for minimizing total operation cost. Some amount of customers' consumption in ToU program are presented as load bidders. Hence, the objective is to maximize deference of end-user cost and operation cost. Accordingly, the objective function for ToU program and their constraints are shown as follows:

   , , , , , , , , , , 1 1 1 , , , , , , , 1 1 1 ( ) (Pr (
) )

B N Up Up Down Down b t b t n t n t n t n t n t n t n n t n n t T b n S N B t up up down down t s s n t n s n t n s b t s n b Bid DisL P B C SR C SR SU y SD z Max r resC r resC V OLL Lshed                                   (1)
In this formulation, the first line is regarding first stage where

N B L dispach n t b t b t l t n b l P W Ld Pflow           (2)
where,

,

dispach
where l X is reactance of line l , ls  is voltage angle of sending-end bus of line l and lr  is voltage angle of receiving-end bus of line l and its constraint is as follows:

max max , , ,
l
Constraints of second stage include DC power flow equation which is as follows:

, , ,

The deployed up-and down-spinning reserve constraints are as follows:

, , , , , , , , , , 0. 0.
Finally, the maximum load that is participated in this demand response program should be specified as follows:

,, b t b t DisL Ld   ( 13
)
where  is the percentage of the load at bus b that should be joined in DRP.

On the other hands, the objective function for ASDR and some constraints have a few differences compared with ToU. In this program, total operation cost will be minimized and demand response is scheduled as a reserve for the loads which participate in this program. the objective function is as follows:

 , , , , , , , , , 1 1 1 , , , , , , , , , 1 1 1 1 )
                                      (14)
where

,
0 0 0 , , , , , , , 1 1 ,
, ,

K kk b t b t b t b t b t k K k k k b t b t b t b t b t b t b t k k k k b t b t b t DR q u u CDR cc q u cc u qq            (15)
In this program, several demand response discrete points k is determined as , , , ,, s b t dr is scheduled demand response reserve of bus b in scenario s at hour t. Some of the constraints are different from the ToU program that are as follows:

, , , , 1 1 1
N B L dispach n t b t b t l t n b l P W Ld Pflow          , , , , , , , , , , , , , , , , , , 1 1 1 1
                (17)

Case Study

The 14-bus IEEE power system is used in this study. The slack bus of system is node1. Two wind farms are connected to the power system at Node 5 and Node8. The capacity of each one is equal to 100 MW. Here are several scenarios that each trajectory is the sum of power production of two nodes. There are four cases to assess running ToU and ASDR and compare them in terms of efficiency. First case is running a stochastic SCUC to maximize social welfare without participating loads as ToU DRP and with a constant tariff for load bidding , bt Bid . The second case is similar to first one with difference of considering 20% of loads for joining ToU DRP with different tariffs , bt Bid . The third case is running a stochastic SCUC to minimize total operation cost without considering ASDR. The fourth case is similar the third one with considering 20% of loads to join ASDR program. In the first case, it is assumed that load bidding is 50$/MWh for all hours. In the second case, two tariff levels are defined for ToU. For peak hours e.g. 13-20 and 28-45, tariff is 40 $/MWh and for others tariff is 70 $/MWh. Accordingly, social welfare for each hour is obtained and it is shown in Fig. 1. As can be seen, in case 2 where the ToU is used the social welfare is mostly higher than case 1, especially in peak times. Moreover, in Fig. 2 which shows the amount of DR participated loads that are supplied. As can be seen, some of loads at peak hours are not supplied in order to get higher social welfare. For the third and fourth cases, demand response is applied just for node 3 and 4. Furthermore, information of ASDR is shown in Table . 1. As can be seen in Fig. 3, the operation cost of forth case at hours 34-38 and hour 42 is lower than the case without ASDR. Because, demand response is just scheduled for load in node 4 at hour 34-38 and hour 42. Therefore, the operation cost at these hours are less than operation cost without considering ASDR. Table 2 shows cost of reserve-up, reserve-down, power production units, and security cost which is related to balancing market for all four case studies. The total cost case 1 which is social welfare is lower than case 2 because of using ToU in second one. Finally, total operation cost for case 4 is lower than case 3 because of using ASDR in fourth case.

Conclusions

In this paper, the impacts of Ancillary Service DR (ASDR) and Time of Use (ToU) were studied. A day-ahead market with a balancing market was considered. With a Monte Carlo Simulation (MCS) method, several scenarios were generated in order to tackle the variability and uncertainties of wind generation. The impacts of merging ASDR and ToU were thoroughly investigated. The results of this program demonstrate that using demand response program leads to not only compensate wind generators uncertainties but also increase operator benefits. In peak hours, volume of supplied loads will be dropped in both DR programs and social welfare will be higher which means both of operator and customer are more satisfied.

  bt W is the real power usage from wind farm node n at hour t which should be less than exp , ect bt W (the expected produced power from wind farm of node n at hour t), , bt Ld is the whole demand at bus b and hour t,  is the percentage of the load which must be feed and are not placed in ToU program, , lt Pflow is real power flow in line l and hour t which is as follows:

  hour t and each discrete point k has a capacity cost , k bt cc . The first k is separated and the difference of next k and previous one is assigned as , level can be selected by the objective function based on profitability. The state of which level k at bus b and hour t is being deployed is determined as , k bt u . This program is almost the same in second stage which is as follows:

  is the energy cost of demand response reserve in scenarios and which level k at bus b and hour t in scenario s is being deployed.

Fig. 1 Fig. 2 Fig. 3

 123 Fig.1 social welfare for case 1 and 2 at each hour

  The second line is related to second stage which handles the balance market by different scenarios where

								Bid , is load
								, bt
	bidding at hour t and bus b,	DisL , is the dispatched load at hour t and bus b.
								, bt
	Moreover,		PB	is cost of scheduled power production of unit n at hour t.
				n	,, t n t
	C	,, Up Up n t n t SR ,	C	,, Down Down n t n t SR
								, ts , is the probability of scenario s Pr
	at time t,	,, up s n r	t			resC	up n	is the cost of scheduled up-spinning reserve of unit n at
	scenario s,	,, down s n t r		resC	down n	is the cost of scheduled down-spinning reserve of unit n at
	scenario s. The last term	VOLL		Lshed	is the cost of forced load curtailment at
								s	,, n t
	bus n and scenario s and hour t.
		Subject to:
		DC power flow equation
								,	,	,	,
							1	1	1

are cost of scheduled up-spinning reserve and down-spinning reserve of unit n at hour t, respectively. n SU , n SD are the start-up cost and shut-down cost of unit n, respectively.

Table . 1

 . ASDR data

	k						0	1	2	3
	() q MW k t		20	30	40	50
	cc	k t	($	/	MWh	)	10	12.5	15	17.5
	ec	k t	($ /	MWh	)	20	22.5	25	27.5

Table . 2

 . Different costs of 4 case studies

		Case1-	Case2-	Case3-Base		Case4-
		Base ToU	ToU	ASDR	ASDR
	Reserve up($)	0	0	0		0
	Reserve down($)	34627	34521.700	34627.000	0	40031.82
	Production ($)	669670	628490	669670		651300
	Security($)	42380	42270	42380		48270
	Total($)	306880	429770	661920		651080

Acknowledgment. This work was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 -POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.


Marco R M Cruz
email: marco.r.m.cruz@gmail.com

Desta Z Fitiwi

Sérgio F Santos
email: sdfsantos@ubi.pt

João P S Catalão
email: catalao@fe.up.pt

Flexibilizing Distribution Network Systems via Dynamic Reconfiguration to Support Large-Scale Integration of Variable Energy Sources Using a Genetic Algorithm

Keywords: Distributed generation, Network reconfiguration, RESs, Genetic Algorithm, Variable Energy Resources

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

Introduction

In recent years, integration of Distributed Generations (DGs) in electric distribution systems has attracted more attention due to various reasons [START_REF] Santos | New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part I: Problem Formulation[END_REF]. The purpose of DG is to install small generation plants (mainly of non-conventional energy sources) close to end-users. Because of the growing concerns on climate changes, increased demand for electricity and other reasons, several states are adopting new policies to support the development of "clean" energy resources and address these concerns [START_REF] Paliwal | Planning of grid integrated distributed generators: A review of technology, objectives and techniques[END_REF].

Nevertheless, dramatically increasing the level of variable Renewable Energy Sources (vRESs) often poses some technical challenges that often undermine the stability and reliability of the system as well as the quality of power delivered to consumers [START_REF] Colmenar-Santos | Distributed generation: A review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks[END_REF]. This is because such intermittent energy sources substantially increase the level of operational variability and unpredictability in distribution network systems, which further complicate the "operation, planning and control" of such systems [START_REF] Santos | New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part I: Problem Formulation[END_REF]. This can be addressed by enhancing the flexibility of existing systems. One way to do this is using dynamic distribution system reconfiguration (DSR). A dynamic DSR can considerably enhance the flexibility of the system and improve voltage profiles, thereby, increasing chances of accommodating large-scale vRES power. The DG placement and sizing problem has been widely studied especially in recent years. In relation to this, authors in [START_REF] Georgilakis | Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research[END_REF] present a detailed review of existing literature in DG allocation and associated issues. Depending on the size and type of DGs, their integration in distribution network systems may have positive or negative effects. Authors in [START_REF] Georgilakis | Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research[END_REF] and [START_REF] Paliwal | Planning of grid integrated distributed generators: A review of technology, objectives and techniques[END_REF] employ several techniques to analyze the impact of DGs in electrical systems as a whole. Some researchers resort to ways that can be used to develop appropriate rules for allocating DGs in distribution network systems while minimizing their possible side effects [START_REF] Hung | An optimal investment planning framework for multiple distributed generation units in industrial distribution systems[END_REF]- [START_REF] Vinothkumar | Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning[END_REF]. In this regard, the most relevant issues that need to be accounted for in DG integration include the network topology, DG capacity and suitable location among others. In the absence of advanced enabling mechanisms such as smart-grids, each connection point in distribution network systems has a maximum DG penetration limit beyond which some of the system performance indicators such as losses and technical issues are undermined [START_REF] Ugranlı | Convergence of rule-of-thumb sizing and allocating rules of distributed generation in meshed power networks[END_REF], [START_REF] Murty | Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth[END_REF]. One of the main aims of this work is to develop appropriate tools for optimally integrating variable energy sources so that the quality of power delivered to end-users is enhanced at a minimal possible cost. To this end, an optimization problem is proposed to optimally deploy such resources in distribution systems, and investigate the impacts of grid reconfiguration on the penetration level of vRESs in such systems. The developed model is solved using a genetic algorithm. A standard IEEE 33-node distribution system is employed in the analysis. Hence, one of the main contributions of this work is an improved mathematical model for jointly optimizing distribution network reconfiguration and allocation of RES-based DGs. Another contribution is the quantitative and qualitative analysis made to assess the impacts of network reconfiguration on the penetration and utilization level of intermittent power, and relevant system variables in the distribution system.

Relationship to Smart Systems

The integration of DGs in electrical distribution network systems (renewable types, in particular) has been gaining momentum since recently. This is driven by a number of factors of technical, economic and environmental nature. Growing concerns related to the increasing demand for electricity, energy security and climate change will further lead to a continuously increasing share of vRESs (such as wind and solar) in the final energy consumption. However, a very high penetration level of such resources results in some technical challenges in the system that may undermine the integrity, stability, reliability and quality of power delivered to consumers [START_REF] Santos | New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part I: Problem Formulation[END_REF]. This is mainly due to the intermittent nature of such energy sources that substantially increase operational variability and unpredictability in the power production. To deal with such challenges, existing systems should be equipped with new flexibility mechanisms. For example, the deployments of smart-grid technologies are expected to adequately overcome the technical challenges posed by RES-based DGs as a result of the dramatically enhanced system flexibility [START_REF] Santos | New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part I: Problem Formulation[END_REF], [START_REF] Ma | The review of smart distribution grid[END_REF]. In connection to this, a dynamic (and possibly automated) distribution network reconfiguration is one of these technologies that are expected to be rolled out in distribution systems for the same purpose. Distribution System Operators (DSOs) can reap the benefits of automated DSR that along with other smart systems helps to address the challenges of DG sizing and allocation problem [START_REF] Zhou | Research review on smart distribution grid[END_REF]. In general, the contribution of the current work to smart systems can be summarized with the following points: 1. DSOs can invest in smart-grid systems that have the capability to automatically switch branches in order to operate the system with reduced costs and improved system stability. 2. After the integration of vRES based DGs, smart systems that have the capability to switch branches can efficiently route RES power generated elsewhere where the demand is low to locations where demand is relatively higher, hence reducing curtailment. All this leads to an enhanced voltage profile and reduced energy losses in the system. 3. Smart grids and isolated systems are often based on vRES technologies.

Innovation in smart systems will influence control and operation of these systems, reducing the impact of the associated variability and uncertainty of such DGs. This in turn implies better performances in terms of stability, reliability and quality of the systems. In the near future, isolated systems may operate automatically, without DSO's intervention. And, this can bring fewer concerns for remotely located systems.

3 Model Formulation

Objective Function

The main objective of the problem is to minimize the total costs of operation in the system, which is given by ( 1).

(

) 1
where Ci is the cost of each population, contained in the fitness vector. The formulation of the problem is based on the AC network model. In order to solve the AC optimal power flow problem, MatPower is used. The combinatorics of DSR is handled via a genetic algorithm. Details of the algorithm are presented below.

a) Initialization: In order to set the connected branches, a binary population is assumed. First, populations whose number is equal to the number of links are generated. The generated populations should keep the network radial, given by the number of buses minus the number of generators. The DG sizing problem assumes integer values. Hence, a DG chromosome generates integer numbers, representing the size of a DG and with a length equivalent to the number of buses to simultaneously handle the allocation problem. This means that each population has the size and the location information of each DG type.

b) Mutation: Uniform crossover, Boolean mutation for the connected branch is used. At the same time, integer mutation for the DG placement and size is used for new populations based on the best populations.

c) Selection: Tournament selection is used to select the best minimal cost. In tournament selection, n individuals of a population are selected randomly with the same probability. The individual with the greatest fitness among them is selected and passed to an intermediate population. The process ends when the intermediate population is fulfilled. Crossover uses the information in two or more individuals to generate one or more individuals. Mutation can diversify when new information is introduced in the individual, and consequently to the population.

Restrictions

In order to keep the radiality of the network system, one needs to check if the generated population fulfils the following criterion: [START_REF] Paliwal | Planning of grid integrated distributed generators: A review of technology, objectives and techniques[END_REF] This condition is applied to check if the criterion is satisfied. This is a necessary condition but not sufficient to obtain a radial topology. It is therefore necessary to verify if all buses in the system are connected. This condition is expressed as follows [START_REF] Hayfa | Optimal Power Distribution System Reconfiguration using Genetic Algorithm[END_REF]:

(3)
where represents the set of nodes in the system; is the unique path connecting nodes and ; and refer to the statuses (0 or 1) of the n branches in the system that constitute the path ; and is an index of the branches that constitute each path. In addition to the above constraints, the lower and upper limits of system variables such as voltage, current, active and reactive power generation are included.

Active and Reactive Power Balances

The constraint related to the active power balance is enforced using Eq. ( 4):

(4)
Similarly, reactive power balance should be ensured, which is given by ( 5):

Deploying reactive-power-only DGs also has some noticeable impact in systems losses, and voltage profiles. In Case 5, the reduction in total costs is 20.65% compared with Case 2 and 21.08% when compared with Case 1. Compared with Case 2, active and reactive power losses are slashed by 76.22% and 66.68%, respectively. As in Case 6, there is no big impact in total costs, only 0.87% when compared with Case 1 but, there is a huge difference in terms of losses. Compared with Case 1, the active and reactive power losses are reduced by 53.04% and 32.69%, respectively. Although the costs are slightly increased, the benefits of having DGs with this technology are evident with the reduction of losses and improvement in voltage profile. The placement and sizing of DGs may not be optimal because of the solution method. However, there are small differences from one generation type to another, probably indicating the closeness of the solution to the optimal one. It seems that there is no connection between Cases 4, 5 and 6 with respect to locating the critical buses to install DG. We can make a connection between Cases 4 and 5 as well as Cases 4 and 6. Recall that Case 4 considers DGs with active and reactive power generation capability while active-power-only and reactive-poweronly DGs are considered in Cases 5 and 6, respectively. Having this in mind, Case 4 and Case 5 seem to have common optimal DG locations including buses 17, 23 and 31. Case 4 and Case 6 also have common "optimal" DG locations such as buses 22, 24, 30 and 32. When we look at the demand and at the total installed size of DG, there seems to be a lot of discrepancies among the different cases. The configuration outcome of Case 2 is shown in Fig. 3. Fig. 4 shows the DG location and size corresponding to Cases 4, 5 and 6. Fig. 5 shows the configuration and DG placement outcomes for Case 4.

Conclusions

In this work, an improved mathematical model has been developed with the aim of performing quantitative and qualitative analysis with regards to the impacts of dynamic network reconfiguration on the penetration and utilization level of variable renewable power and overall system performance. The developed model has the overall cost minimization as an objective, and is based on the most accurate AC network model. A genetic algorithm has been used to efficiently handle the combinatorial nature of the reconfiguration problem. Generally, the outcome of the analysis shows the capability of dynamic network reconfiguration in increasing the penetration and utilization level of vRES as well as improving the overall system performance in terms of enhanced voltage profile, reduced costs and losses.

Fig. 1 .

 1 Fig. 1. Voltage profiles corresponding to Cases 1 and 2.

Fig. 2 .

 2 Fig. 2. Voltage profiles corresponding to Cases 1, 4, 5 and 6.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Network topology after reconfiguration in Case 2

Fig. 5 .

 5 Fig. 5. Network topology and DG placement in Case 4.

Table 1 .

 1 Values of relevant system variables corresponding to the different cases.

	Cases	Total	Total Active	Total	Reactive	Total installed	Computatio
		Cost	Power	Power	Losses	DG	size	n time [s]
		[€/h]	Losses	[Mvar]		[MVA]
			[MW]
	1	228.181	0.1865	0.0999		0		-
		6
	2	226.946	0.1249	0.0825		0		24.423398
		3
	3	226.959	0.1256	0.0830		0		27.102581
		3
	4	179.738	0.0023	0.0020		23		32.926209
		5
	5	180.074	0.0443	0.0333		17		39.599780
		7
	6	226.195	0.0876	0.0672		15		34.074488
		4

Table 2 .

 2 Branches opened and DG location in the 33-bus distribution system

	Cases	Opened branches	DG Bus Location
	1	21-8; 9-15; 12-22;18-33;25-29	-
	2	7-8; 9-10; 14-15; 32-33;25-29	-
	3	7-8; 10-11; 14-15; 32-33; 25-29	-
	4	7-8; 11-12; 15-16; 21-22; 28-29	4; 9; 16; 17; 20; 22; 23; 24; 26; 30; 31; 32
	5	6-7; 11-12; 14-15; 26-27; 32-33	5; 8; 12; 13; 14; 17; 23; 25; 28; 31; 33
	6	7-8; 8-9; 14-15; 28-29; 32-33	5; 6; 13; 15; 22; 24; 30; 32

Acknowledgment. This work was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 -POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013. The research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.

 [START_REF] Paliwal | Planning of grid integrated distributed generators: A review of technology, objectives and techniques[END_REF]where and refer to the voltage angle and magnitude; and are generator injections; and : load injections; : a matrix whose element is 1 if generator j is located at bus and 0 otherwise.

Tests and Results

To perform the analysis in this work, a standard 33-bus radial distribution network, taken from [START_REF] Cruz | Benefits of Coordinating Distribution Network Reconfiguration with Distributed Generation and Energy Storage Systems[END_REF], is employed. The rated voltage, active and reactive power demand in this system are 12.66 kV, 3.715 MW and 2.3 MVAr, respectively. Further information about this system can be found in [START_REF] Cruz | Benefits of Coordinating Distribution Network Reconfiguration with Distributed Generation and Energy Storage Systems[END_REF], [START_REF] Cruz | Influence of Distributed Storage Systems and Network Switching/Reinforcement on RES-based DG Integration Level[END_REF]. The voltage at any node in the system is allowed to deviate up to maximum value of ±5%. All DGs are assumed to have a power factor of 0.95. The costs of the generators at the feeders given by polynomial functions . For integrating the DG as a PV bus and add to the cost of the system given by the OPF, one polynomial function was taken into consideration . For the sake of simplicity, the availability of wind and solar power sources is assumed to be uniform across the nodes in the system. Furthermore, Node 1 is considered as the reference node, and a base power of 100 MVA is used. A total of six cases, designated as Case i, are considered in the analysis. Case 1 is the base case; Case 2 considers reconfiguration; Case 3 is a scenario where minimizes only losses. Cases 4, 5 and 6 all handle reconfiguration along with DG integration but they differ in that, in Case 4, the considered DGs are capable of producing active power as well as injecting and absorbing reactive power, Case 5 considers DGs that can only produce active power, and the DGs considered in Case 6 are capable of only producing or consuming reactive power. The variations of system parameters when considering different are analyzed as shown in Table 1. Comparing Case 1 with Case 2, we see that reconfiguration slightly lowers the total costs and losses. The total cost reduction is about 0.54%. The active and reactive power losses are also reduced by 61.59% and 17.38%, respectively. In Fig. 1, the voltage profiles of the system after reconfiguration and the base case are shown. Clearly, the positive contribution of reconfiguration to the voltage profiles can be observed. In addition, in Table 1, there is little difference between minimization of losses and minimization of costs, the difference is approximately 0.0057% for total costs, 0.5147% for active power losses and 0.5695% for reactive power losses. In Table 2, the only difference lies between the opened branches that is 9-11 in Case 2, and 10-11 in Case 3. Only one branch is different, and almost leads to similar fitness function values. There is a small difference and we can conclude that these configurations are minimized but may not be the global optima. Analyzing the results in Table 1 further, there is a significant difference in total costs and in total losses in Cases 4 and 5 compared to the first three cases. The effects of having DGs capable of producing only active or reactive power are also seen in Fig. 2. With active-power-only DGs, we can also have a better voltage profile, not as linear as in Case 4 but significantly better than in the base case.