Eduardo M G Rodrigues

Radu Godina

Tiago D P Mendes

João C O Matias

João P S Catalão
email: catalao@ubi.pt

Influence of Large Renewable Energy Integration on Insular Grid Code Compliance

Keywords: Grid codes, renewables penetration, insular energy systems, smart grid, cloud computing

Large-scale deployment of renewables in island energy systems attracts local attention of grid operators as a way of reducing fuel fossil consumption. Planning a grid based on renewable power plants poses serious challenges to the normal operation of a power system, namely on frequency and voltage stability. In past grid code compliance, wind turbines did not require services for supporting grid operation. To shift to large renewable energy integration, the island grid code should incorporate a new set of requirements in order to regulate the inclusion of these services, which is the aim of this paper. The paper also discusses additional requirements such as "virtual" wind inertia.

Introduction

Renewable integration (especially wind energy) on mainland energy systems has been increasing significantly in the last decade [1]. However the penetration rate in insular energy systems is still low. Presently, high penetration of renewables is considered extremely challenging for normal grid operation by the local grid operators. Typically the islanded energy system has a small size, low interconnections and not a high shortcircuit ratio (SCR). Therefore, it is more vulnerable to frequency and voltage stability issues [START_REF] Wang | Operational adequacy studies of power systems with wind farms and energy storages[END_REF] and since the generation units cannot be very large, its robustness is ensured by maintaining large reserve capacity, which raises electricity generation costs. Local grid operators are now aware that there is a significant potential for exploring renewable energy resources, which could reduce external dependency on imported fossil fuel [START_REF] Kavadias | The contribution of renewables on reducing the electricity generation cost in autonomous island networks[END_REF]. However, it is well known that the intermittent nature of wind and solar energy cannot be controlled. This variability on seasonal and daily basis has been seen as a serious obstacle for large scale installation of renewable power plants. This paper discusses grid code requirements for large-scale integration of renewables in an island context as a new contribution to earlier studies.

Contribution to Cloud-based Engineering Systems

Future insular power systems that rely on a heavy mix of distributed energy resources (DER) will require managing tools based on real-time monitoring, controlling and high computational power resources. Cloud computing proposes advances services which allow to use its massive computation power ability and the scalabilities relying on large scale servers that host the web services and web application. Similarly, cloud computing can provide physical or virtual resources on which the insular power system applications can run. On the DER point of view, local hardware facilities for executing specific software programs become practically residual. Thus, it reduces the installation and maintenance costs. Via a cloud structure each client can always access its respective applications or data, out from anyplace and by a connected device to the network. In addition, it provides a common platform for sharing data which drives increased cooperation between insular grid parties. On the other hand, for the network operator side, cloud-based grid management services create a unique framework for the design and implementation of a centralized structure for their distributed monitoring and control tasks. Therefore, by integrating cloud-based engineering systems in the insular power grid infrastructure, an efficient, reliable and secure energy distribution can be accomplished.

Island Power Generation System Overview

The island power generation infrastructure typically consists of diesel generators, small groups of thermal power plants, and may include natural hydro or thermal sources based production. Basically there are two types of units. One class of generators runs at constant output, known as base load units, while another group of generators has the function of adapting its output following the load needs. Wind and solar generators do not fit in any of these two groups because they are not flexible load following units. Generators like load following units are designed to operate with variable output. Moreover, this type of flexible generation must maintain a considerable amount of active power reserve for frequency regulation. In an island energy system this reserve is much more critical because conventional plants have a smaller size when compared to power installations in the mainland energy system. Replacing flexible load following units by renewable generating units implies that there will likely be less kinetic energy exchange to support grid power balance, resulting in a degradation of frequency regulation capability, which in turn may imply larger and faster frequency deviations [START_REF] Morren | Inertial response of variable speed wind turbines[END_REF]. Hence, the minimum level of conventional generation and concerns about grid stability creates a practical restriction on planning more renewables integration [START_REF] Papathanassiou | Power limitations and energy yield evaluation for wind farms operating in island systems[END_REF]. Even in low penetration scenarios, it is not uncommon to see power curtailment actions by the system operator to face renewable power excess on the grid, or even shutting down the wind farm due to a grid disturbance. However, the revenue lost due to curtailment or disconnection may be greater than the cost of maintaining conventional generation. To accelerate renewable integration, wind and solar farms should replicate conventional power plants during and after network faults [START_REF] Jauch | Stability and control of wind farms in power systems[END_REF]. To become effective, island regulations must be updated to impose this strict operation profile. Grid code requirements have been studied for large wind farms integration in mainland systems [START_REF] Iñigo | Connection requirements for wind farms: A survey on technical requirements and regulation[END_REF]- [START_REF] Díaz-González | Participation of wind power plants in system frequency control: Review of grid code requirements and control methods[END_REF]. Yet, technical literature focusing on island grid codes requirements is still very scarce and, therefore, this paper provides a new contribution to this issue. In essence, present insular regulations do not allow the grid operator to control distributed energy resources (DER). The coordination between transmission system operator (TSO) and distribution system operator (DSO) is insufficient for the effective DER integration. The lack of adequate regulation for DER systems connection is also a reality. Further, DER does not have any economic incentive to take part in the network operation. All these issues are taken into account in this paper.

Grid Code Requirements for DER

A grid code serves the mission of defining the physical connection point requirements to be followed by energy production equipment in order to connect to the grid. In addition it should provide rules for renewable power plants to support grid stability.

Regulations for Continuous Operation

A residual balance mismatch is fairly normal which may result in over-frequency as well as under-frequency. With intermittent generation at larger-scale, the balancing game becomes more complex and less predictable. A proactive attitude is required regarding DER system to cope with these issues through a well-defined specifications-based behavior.

Voltage and Frequency Operation. Renewable power plants must stay connected to the grid and able to withstand voltage and frequency variation limits established by the island grid system operator. Voltage range is related to the size of the transmission system, which is characterized by different voltage levels. Frequency nominal range is determined by the electrical characteristics of the insular energy system, such as overall inertia of the installed power generation infrastructure. Renewable power sources must also face voltage and frequency deviations outside the conventional range during a short time period and without being tripped.

Active Power Regulation.

It is a set of power control strategies that gives freedom and flexibility to the system operator to manage the power output injected into the grid by renewable power plants. Wind and solar power plants should comply with this requirement. As the renewable generation is increased, active power regulation requirements become indispensable on island grid codes.

a) Maximum power limitation

This parameter has the purpose of setting the maximum power output of renewable generation below its power rating. By restricting the maximum amount of power injection, the system operator has a way to prevent more instabilities of active power balance due to the unpredictable nature of wind and solar resources.

b) Active power range control

Renewables cannot deliver dispatchable power on demand. The goal is to replicate traditional power sources that are fully controllable. Renewable generating units should be prepared to curtail artificially the power production to maintain power balance and also, if necessary, to contribute to the stabilization of grid frequency. Both requirements have different implementation scopes. The first one permits the TSO to introduce output power dispatch flexibility on wind power plants (WPP) and on solar power plants (SPP). The second is basically extending primary control function to the renewable plants.

c) Ramp rate limitation

Limiting the power gradient of renewable power through a set-point can be a very effective way to minimize the impact of a sudden rise of renewable generation. Ramp rate is defined as the power change from minute to minute (MW/min). The idea of this concept is to filter faster variations of wind power output by imposing a ramp rate according to the changes observed on power demand.

d) Delta control

It is a way of securing spinning reserve based on renewable power generation. Power output is artificially lowered, below the available power at the moment of generation. The difference is kept as reserve to be used like a conventional generation does (primary and secondary control). However, the curtailed power depends on available wind or solar power. Thus, the level of reserve is not constant. The curtailed power can be released not only for frequency regulation, but also to maintain the voltage of the overall system through the injection of reactive power to grid.

Power-Frequency Response. When an energy unbalance occurs the frequency deviates from its nominal value. As the unbalance grows a large deviation of frequency is expected to happen, threatening normal power network operation. In order to confine deviation extension to safe levels, frequency surveillance and corrective actions are performed by conventional generators (primary control) along with grid operator supervision and, if necessary, authorizes spinning reserve release (secondary control). For the specific case of European islands, frequency regulation capability compliance is not specified by local grid codes. Wind farms able to restore generation/demand balance are already required in some European countries. Typically, a mainland TSO imposes a frequency regulation strategy through a power frequency curve specification only addressing wind power plants operation. Nevertheless, no compliance is directly demanded for solar power plants on these regulations. Since insular grid codes in European space didn't yet evolved to require this behavior, formal analyses have to be carried out observing the most advanced specifications at non-insular territories. Fig. 1 shows power-frequency response curves required in Ireland, Germany and Denmark [START_REF]Transmission Code 2007 -Network and System Rules of the German Transmission System Operators[END_REF]- [START_REF] Eirgrid | EirGrid Grid Code -Version 5[END_REF]. It is clear that frequency support behavior differs from country to country. The range for frequency correction is higher on Danish and Irish grids. Both regulations set a dead band range where active power production remains independent of frequency variation. Beyond this band both codes show different interpretations on how a wind farm should react to frequency deviations. According to the German regulation, a renewable power plant has to curtail active power by starting at 50.2 Hz with a gradient of 40% of available generation at the moment per Hertz. As for the Irish code, when in the presence of over-frequency excursions, a renewable power plant reacts according to a power curtailment gradient set by the grid operator. Afterwards, if necessary, curtailment rate is updated to the grid operator needs. With respect to frequency events, the wind farms response in German networks is limited when compared to Danish and Irish requirements, because the regulations establish that energy production can be artificially kept lower to provide secondary control at lower frequencies. Furthermore, Danish rules also allow the grid operator to smooth wind power output of each wind power plant, adapting individually power frequency response to the needs. For this purpose, three curtailment gradients that reproduce a droop controller action can be configured over the Danish curve. In Fig. 1, droop regulation areas are identified as d1, d2 and d3. Similar flexibility is provided by Irish regulation.

Reactive Power Control. Traditionally, synchronous generators had the exclusive task of ensuring bulk system voltage regulation at transmission level. As for the distribution network, voltage regulation still remains under distribution substation control. Since their introduction, wind farms were built to generate only active power. Thus, they were operated to keep the power factor at unity. In the last decade this trend has changed. Most of the European grids on mainland context have introduced new regulatory conditions, extending reactive power capability along with active power generation to wind and solar power. Likewise, the growing dissemination of renewables-based DER systems in insular systems will force the incorporation of this ancillary service for security reasons as a result of strong technical constraints. Since an isolated power network is different from island to island, reactive power needs have to be addressed to meet local interconnection issues. This requirement comes in three different ways: by assuming a Q set-point, by power factor control or by managing power reactive flow as a function of grid voltage. The insular approach, for now, relies basically on power factor band specification that must be provided by the wind farm under normal operation. Again, instructions concerning solar power plants are completely absent in the regulations under analysis. Common range goes from 0.95 lag to lead at full active power, having voltage range within 90% to 110% of nominal value. Other ranges may be found, such as 0.86 inductive to unity power factor.

Given that the alternative ways to express reactive power support are not imposed by insular grid operators, it is necessary to conduct the analysis relying on mature standards, such as occurs in continental Europe. The Q set-point related to active power generation is one of the strategies adopted in Danish code. It distinguishes the support level according to the wind farm global rating power. Fig. 2 indicates that at power levels above 20% of the rated power, the wind farm whose rating exceeds 25MW has to provide an additional 33 % of reactive power, as maximum, to the instantaneously available active power output. Whereas, smaller wind power facilities have to provide an additional 23% of reactive power. The criterion for reactive range requirement must cover Q capability over the full output range through the specification of Q-P diagrams. However, it should be pointed out that during periods of reduced wind or solar production the reactive power capability is lower. When the active power output is low and doesn´t surpass a certain threshold, a less strict reactive power range could be imposed to solar or wind power plants under the grid code requirement. The third way of reactive power compensation is illustrated in Fig. 3, using as example the framework directives of the German regulator. The reactive power compensation range targets diverse voltage levels on the power system, from distribution to transmission networks. Renewable generation units must meet the reactive power capability within the area specified by the TSO, which is free to choose between three variants on the basis of relevant network requirements. In addition, the grid operator may alternate between the Q-V variants over time, whenever it is required. Reactive power compensation acting directly on grid voltage deviation has some merits when compared to the other reactive power compensation approaches. The strongest argument relies on terminal voltage limitations that could affect reactive power generation of variable generators, including renewable power sources [START_REF] Ellis | Reactive Power Performance Requirements for Wind and Solar Plant[END_REF]. The criterion for reactive range requirement must cover Q capability over the full output range through a specification of Q-P diagrams. Since grid voltage level has a direct impact on renewable power source ability to provide reactive power, the Q-V diagram could also be required as a grid code specification in order to minimize its effect [START_REF]Reactive power interconnection requirements for PV and Wind plants[END_REF].

Emulating Inertia of Conventional Power Plants. "Virtual" wind inertia is a relatively new concept that can expand power-frequency response capability of a wind turbine to further improve grid frequency stability. The concept relies on using the kinetic energy stored in rotating masses of the wind turbine. Despite wind turbine technologies have different inertial response performances [START_REF] James | Frequency Response Capability of Full Converter Wind Turbine Generators in Comparison to Conventional Generation[END_REF] a recent study has shown that "virtual" wind inertia can exceed the inertial power response of a synchronous generator with the same amount of inertia [START_REF] Keung | Kinetic Energy of Wind-Turbine Generators for System Frequency Support[END_REF]. This requirement should be seen as an additional reinforcement of primary power-frequency response of a wind turbine.

Special Requirements under Network Disturbances

While conventional power plants, such as based on synchronous generators, have strong strength capabilities to resist to symmetrical and asymmetrical faults without being disconnected, wind power plants were not initially designed to handle grid faults. From an insular grid stability point of view, an unexpected shutdown of a wind farm may slow power system recovering because there is less generation to support it. For grid security reasons, renewable power plants should be tolerant to faults, at least during a short time, enough for fault clearance.

Fault Ride through Capability. It is the ability of a generator to survive a transient voltage dip without being tripped. Compliance with this requirement means the generator must be able to ride through all kinds of grid faults, including faults with very low remaining voltage levels and unsymmetrical (1-phase and 2-phase) faults. FRT capability is normally specified through a voltage-against-time profile, which establishes minimum phase-to-phase voltages at PCC for a maximum time frame. Typically a FRT requirement defines three main areas of response depending on voltage deviation severity and time duration of the anomaly. Voltage drop down to 90% of the voltage in the point of connection is not considered significant enough to damage the generator. Dropping below this value a generic FRT requirement establishes a maximum voltage drop at which a generator is forced to stay connected to the grid for a maximum exposure time, independently of the grid fault severity level and the number of faulty phases involved on the grid voltage anomaly. Between the maximum and minimum voltage drop, the exposure time to grid fault is extended as voltage deviation is lower. For voltage-time value pairs below the FRT line, the generator may be instructed to show a specific behavior before being tripped. Two grid codes were chosen to highlight a typical FRT curve. Fig. 4 shows mandatory profiles described on Danish and German regulations. It should be noted that only the German regulatory framework aims to implement the requirement on a global sense, without targeting a specific renewable power source type. German grid code is in comparative terms the most demanding one. A full short-circuit at PCC must be supported by the wind turbine for a time window of 150 ms, which is a typical operation time of the protection relays while for the Danish network the anomaly tolerance is extended to 500 ms, but it is less severe in terms of voltage drop. It should be noticed that for the German case, wind farms may prolong their connection below the blue area upon agreement. As regards the insular European codes, low voltage ride-through compliance is already imposed in large insular systems such as the Canary archipelago (Spain) or the Crete Island (Greece), where renewable power penetration has grown over the last decade. Fig. 5 presents FRT specifications for Canary Islands, Crete and French Islands [START_REF]Référentiel Technique SEI REF 04 (V5) -Protection de decouplage pour le raccordeent d'une production decentralisee en HTA et en BT dans les zones non interconnectees[END_REF]- [START_REF] Eléctrica De Espana | Requisitos de respuesta frente a huecos de tensión de las instalaciones eólicas[END_REF]. Comparatively to the mainland grid codes previously analyzed, insular voltage-time curves look very similar. Of the three codes analyzed, only in the Canary Islands wind power plants have to withstand instantaneous voltage drop down to zero for a time duration of 150ms. Extensive research concerning the ride-through control capability of wind turbine systems, and other DG systems as well, have showed that with adequate strategy control it is feasible.

Reactive Power Response. Since voltage recovering is a critical point, an additional effort is also required to the non-dispatchable power sources: a reactive current injection mechanism has to be activated as long as fault recovering is underway, followed by a progressive re-introduction of active power after the fault clearance. This capability is seen as crucial for a faster power system recover. This issue is even more critical if we consider that for an insular power network, due to its size and the absence of strong connections, any disturbed operating condition is immediately observed in every corner of the network. Moreover, island grids are characterized by low short circuit power, which exacerbates the instability, contributing for example to a high variation of voltage when a defect appears. Therefore, a quick mitigation of the under-voltage phenomena is essential to normalize active power generation in order to ensure the power balance within the grid, consequently keeping the frequency in a normal range. Insular grid codes evolution on this topic is still scarce. However, the starting point for the introduction of this requirement has already been taken.

In Fig. 6, the rules in effect for the Canary Islands (Spain) are depicted, along with two non-insular equivalent specifications. For the insular Spanish grid code the wind turbine consumes reactive power as long as the voltage recover is underway. Also, for a drop voltage higher than 50% of the nominal voltage, reactive current does not surpass 90% of the global current injected on the grid. Moreover, the figure reveals that there is no reactive power absorption until full voltage recovery. These constraints prevent additional instability on the voltage line. A visual comparison with the other two requirements leads to the conclusion that the German grid code is by far the most severe in terms of reactive power injection, forcing the wind power turbine to delivery only pure reactive current. generation based paradigm. The link should be permanent, dependable and providing bidirectional communication capabilities. From system operator side through the control center, customized dispatch orders are sent to renewable power plants. On the other side, every distributed generation (DG) unit reports its operating conditions to the island operator system. Then, according to the received information, dispatch orders are updated to meet power demand and to stabilize grid electrical parameters.

Conclusions

In this paper, the technical requirements for large-scale integration of renewable energy sources were discussed regarding their incorporation in island grid codes, as a new contribution to earlier studies. By adding control and regulation capabilities to renewable systems, they change from a passive participation on the grid to an active role, providing services only normally found in conventional power plants. Insular power grid reform into a smart grid infrastructure was also addressed in this paper. Emphasis was given to the grid agents/operators new role and to the distributed communication network, as a catalyst of the renewable revolution alongside grid code evolution.

Fig. 1 .

 1 Fig.1. Power-frequency response required by mature grid codes in mainland networks.

Fig. 2 .

 2 Fig. 2. Danish P-Q interconnection requirements for wind power plants.

Fig. 3 .

 3 Fig. 3. Reactive power capability requirements for German case.

Fig. 4 .

 4 Fig. 4. FRT interconnection requirements for German and Danish codes.

Fig. 5 .

 5 Fig. 5. FRT curve examples required in European insular power systems.

Fig. 6 .

 6 Fig.6. Comparison of reactive power requirements during a voltage disturbance.

Acknowledgment. This work was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.

Insular Smart Grid

A large renewable integration means a considerable dispersion of variable power production (VPP) of small scale, scattered across the island landscape. A complete change from a traditional organization to a network based on DER requires a new whole concept to operate the bulk electricity power. The smart grid concept embraces a set of advanced features, among them, monitoring and controlling of each grid element, whatever is the production entity or the physical distribution link, including real-time consumption tracking as well. Isolated networks being prone to stability issues are well suited to evaluate smart grid technologies. Smart grid operating strategies are today being studied in diverse insular regions in detriment of pilot projects on large interconnected systems. The successful implementation of insular Smart Grids implies meeting two key milestones: reinvention of grid operator's role and the establishment of a reliable and efficient communication backbone.

Transmission/Distribution System Operators

Grid agents will have to evolve to adapt their role with the development of a Smart Grid related infrastructure, as well as their interactions with new players of the energy system, which are the renewable DER systems. The nature of the interactions can be described as key services to enable a reliable, secure and efficient exploration of the insular grid (Table 1).

Table1. Grid agent's role toward smart grid integration for an insular power system.

Communication and Supervisory Control

The information exchange between the VPP control center and the power plant is critical, promoting a successful change from centralized generation to distributed


E Heydarian-Forushani

M H Golshan

M Shafie-Khah

João S Catalão
email: catalao@ubi.pt

M E H Golshan

J P S Catalão

Optimal Behavior of Demand Response Aggregators in Providing Balancing and Ancillary Services in Renewable-Based Power Systems

Keywords: DR aggregator, demand response, ancillary services markets, renewable energy resources

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

Introduction

Increase of environmental conservation concerns and decrease of fossil fuel resources have caused penetration of renewable energy resources to be significantly augmented all over the world [START_REF] Leão | The future of low voltage networks-moving from passive to active[END_REF]. Due to the limited predictability and associated uncertainty of these resources, renewable-based electricity systems are confronted with instability problems [START_REF] Shao | Demand response as a load shaping tool in an intelligent grid with electric vehicles[END_REF]. In such power systems, implementation of Demand Response Programs (DRPs) not only can improve the system stability but also can enhance market efficiency, decrease peak demand, reduce price instability, and increase the system reliability [START_REF] Palensky | Demand side management: demand response, intelligent energy systems, and smart loads[END_REF]. The enhancement in market efficiency enables most of market participants, such as transmission system owner, distributors, and retailers to take advantages of implementing DR [START_REF] Strbac | Demand side management: Benefits and challenges[END_REF]- [START_REF] Yousefi | Optimal real time pricing in an agent-based retail market using a comprehensive demand response model[END_REF].

On the other hand, by implementing Cloud-based Engineering Systems (CES) the utilization of Demand Response (DR) will be increased and consequently DR will play a more crucial role in the future. Development of CES can significantly facilitate the aggregation of DR. Therefore, it can be an effective solution to increase the participation of electricity consumers to electricity markets [START_REF] Palensky | Demand side management: demand response, intelligent energy systems, and smart loads[END_REF]. To this end, both technology (e.g. CES) and policy (e.g. market rules and regulations) infrastructures are required to support implementation of DRPs in electricity markets [START_REF] Goel | A framework to implement supply and demand side contingency management in reliability assessment of restructured power systems[END_REF], [START_REF] Aalami | Demand response modeling considering interruptible/curtailable loads and capacity market programs[END_REF]. By developing the mentioned infrastructure, DR aggregators can efficiently take part in different electricity markets such as energy, balancing and ancillary services. In such situations, development of CES can definitely support DR aggregators to participate in the mentioned electricity markets as an important linking participant between Independent System Operator (ISO) and end users.

The literature covers many works regarding responsive demands who bid to the electricity markets [START_REF] Goel | A framework to implement supply and demand side contingency management in reliability assessment of restructured power systems[END_REF]- [START_REF] Moghaddam | Flexible demand response programs modeling in competitive electricity markets[END_REF]. However, DR aggregator performance has not been addressed in the reports. Although the participation of DR providers in the Demand Response eXchange (DRX) market has been presented in [START_REF] Heydarian-Forushani | Risk-constrained offering strategy of wind power producers considering intraday demand response exchange[END_REF], the participation in energy and ancillary services markets has not been addressed. In [START_REF] Shafie-Khah | A stochastic multi-layer agent-based model to study electricity market participants behavior[END_REF], the DR aggregator has been considered as an agent in the electricity market modeled by multi-agent systems. However, the participation of the agent in ancillary services markets has not been modeled. It is noticeable that the simultaneous participation of DR aggregators in the energy, balancing and ancillary services markets has not been reported in the previous works, which is a new contribution this paper provides. In the renewable-based power systems, optimal behavior of these participants is complex due to many uncertainties. The major proportion of the energy is cleared in day-ahead session. Therefore, all market participants have to submit their offers for all hours of the day ahead, several hours in advance. Because of the unpredictable nature of renewable-based power systems in the future smart grid, the offers have a meaningful degree of uncertainty. Thus, employing the balancing markets and ancillary services market is crucial for market operators to supply the spinning reserve requirement. On this basis, the DR aggregators have the opportunity to participate in the mentioned markets and supply a proportion of required spinning reserve and regulation.

In this paper, a model has been developed to optimize the behavior of a DR aggregator to simultaneously participate in energy, balancing and ancillary services markets. To this end, the DR aggregator optimizes its offering/bidding strategies for the mentioned markets based on the contracts with its customers. In the proposed model, uncertainties of renewable energy resources and the prices of electricity markets are considered. Numerical studies show the effectiveness of the proposed model. In addition, Conditional Value at Risk (CVaR) is incorporated into the model to tackle the uncertainties of market prices and the behavior of consumers.

Contribution to Cloud-based Engineering Systems

Due to significant growth of the importance of energy conservation and environmental protections, DR can favorably affect the future smart grid [START_REF] Hajati | Optimal retailer bidding in a DA market e a new method considering risk and demand elasticity[END_REF]- [START_REF] Aazami | A demand response based solution for LMP management in power markets[END_REF]. In this context, Cloud-based Engineering Systems (CES) certainly have meaningful impacts on the future smart grid by increasing the collaboration between market players from both electricity and information viewpoints.

In this context, since a large number of managing and controlling data in the network imposes market participants to employ new computational methods to mitigate the system operation time, the CES can have the most important application.

The CES strongly influences the developing network impact while it depends on the access to numerous computational resources (i.e. cloud) in order to tackle the environmental restrictions by using the scarce processing and information storage proficiency. On this basis, improvements in CES enable end-users to more efficiently behave in demand side programs. DR aggregators possess the technology to perform DR and they are responsible for the installation of the smart meters at the end-users' components. As a matter of fact, DR aggregators can provide connectivity communication capabilities to end-users' components in order to connect them to the cloud. This can reduce the technical complexity and the required efforts to increase the local computational resources at the level of each end-user's component. On the other hand, CES can improve the security of the mechanisms and consequently increase the robustness of collecting data by the aggregator. Since each aggregator represents a significant amount of total demand in the DR market, it can negotiate on behalf of the end-users with the operator more efficiently. Since these players are the link between customers and electricity markets, they have a critical role in moving towards the future smart grid. In the future smart grid, by developing the CES and consequently increasing the participation of customers in DRPs, the players will have a more important role in the electricity markets.

Modeling the Self-Scheduling of DR Aggregators

DR aggregator aims to maximize its profit by participating in day-ahead energy, balancing and spinning reserve markets. A schematic of DR aggregator presence in the mentioned markets, including both renewable and thermal Generation Companies (Gencos) in the supply side and retailers and large customers in the demand side, is illustrated in Fig. 1.

Uncertainty Characterization

In this paper two major types of uncertainty have been considered: the uncertainties related to market prices and the uncertainties associated with the quantity of activated reserve by ISO. The modeling of these uncertainties is explained as follows.

Modeling the Uncertainty of Market Prices. In order to participate in the renewablebased electricity market, the DR aggregator has to forecast market prices. In this paper, three uncertain market prices are considered: day-ahead energy, balancing and spinning reserve. To this end, the Roulette Wheel Mechanism (RWM) technique [START_REF] Amjady | Stochastic multiobjective market clearing of joint energy and reserves auctions ensuring power system security[END_REF] is applied for scenario generation. In order to develop an accurate and appropriate model, market prices have been characterized by log-normal distribution in each hour [START_REF] Conejo | Price-taker bidding strategy under price uncertainty[END_REF]. Thus, considering that µ and σ represent the mean value and standard-deviation, respectively, the Probability Distribution Function (PDF) of market prices can be formulated as follows:

µ µ σ σ σ π   - = -       (1)
Modeling the Uncertainty of Activated Reserve by ISO. In order to model the probability of quantity of activated reserve by ISO,

Res ts

Act , it is considered to be uniformly distributed between 0 and the offered amount by the DR aggregator. On this basis, the PDF of quantity of reserve activated by the ISO can be formulated as follows:

( ) 1 , 0

Res Res ts ts

f x P x P = ≤ ≤ (2)
Considering (2) different outcomes of ISO's behavior for calling the DR aggregator and the activated quantity of reserve are taken into account by the RWM-based scenario generation process [START_REF] Amjady | Stochastic multiobjective market clearing of joint energy and reserves auctions ensuring power system security[END_REF].

Incorporating Risk Management

The uncertain behavior of DR aggregator's customers enforces the profit of this participant with a high risk. Based on this, the DR aggregator has to manage the mentioned risk. To this end, Conditional Value-at-Risk (CVaR) as an appropriate technique is employed to incorporate risk management into the problem. The CVaR can be formulated as follows:

1 : . , 0 1 N S s s s s Max ξ ρ η η α = - ≥ -∑ (3) 0 s s B ξ η -+ -≤ (4)
The parameter α is usually assigned within the interval of 0.90 to 0.99, and in this paper it is set to be equal to 0.95. If the profit of scenario s is higher than ξ , the value of s η is set to 0. Otherwise, s η is assigned to the difference between ξ and the related profit. The above constraints are employed to unify the risk-metrics CVaR.

Mathematical Model of the DR Aggregator

The DR aggregator offers a specified quantity in day-ahead energy and spinning reserve markets in order to obtain an accepted level of energy and ancillary services into day-ahead market for each hour. Then, it can submit new energy offers or update the previous ones in the balancing market. Moreover, in the proposed stochastic framework, risk aversion is implemented by restricting deviations of expected profit using the CVaR technique. According to the above mentioned description, the optimal offer of the DR aggregator at each hour can be obtained by solving the selfscheduling problem to maximize its profit. The objective function can be expressed as ( 5):

π π π π ρ ρ ρ β ξ ρ η α π ρ ρ π π π = = + + - -   + +   +   = + -   - - -     - + ∆ - ∆   ∑ ∑ 1 N S =       ∑ (5)
where, β is the weighting factor to achieve a trade-off between profit and CVaR.

The first term of (5) denotes the income of taking part in the day-ahead energy market. The next two terms represent the income resulting from spinning reserve and balancing markets. The fourth part represents the income resulting from delivering energy while it is called by the ISO. The fifth term denotes the purchase cost of energy from the balancing market that results from not delivering the activated reserve to the grid. The sixth term denotes the cost of responsive demands. The next two terms represent the positive and negative profit resulting from the balancing market and the last term indicates the CVaR multiplied by β .

The objective function is maximized considering the constraints described as follows:

DA

Res bal ts ts ts t

P Act P D + + ≤ (6) 1 .
P P P Act Act D r r π π π π ρ ρ ξ η π ρ ρ π π π + + -- =   + + + - + -≤   - - - + ∆ - ∆     ∑ (7)
∆ = - t t t ω ω ω + - ∆ = ∆ -∆ (12)
The total capability of the aggregator in all day-ahead, balancing and ancillary services markets are given in [START_REF] Yousefi | Optimal real time pricing in an agent-based retail market using a comprehensive demand response model[END_REF]. Eq. ( 7) represents the incorporation of risk into the problem. Constraints ( 8)-( 10) are limits on the offer of DR aggregator, based on the contribution in different markets. Equations ( 11) and ( 12) are employed to calculate the energy deviation using the actual injected energy.

Numerical Study

Some numerical studies are accomplished on the common load curve of a real-world system [START_REF] Shayesteh | A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program[END_REF], in order to indicate the usefulness of the proposed model. It is assumed that the peak of the typical load curve is equal to 100MW. Various scenarios have been generated by means of the probability distribution function which is explained in Section 3. On this basis, all uncertainties of market prices and calling by ISO have been divided into the hours of the day. Changes in β have been accomplished to study the impact of being risk taker or risk averse. Considering β =0 means the DR aggregator takes no risk. The results are expressed in Table 1.

It can be concluded from Table 1 that, as the risk coefficient increases the total expected profit is reduced, but the violation from this profit value is also reduced. In other words, if a risk-averse DR aggregator makes a decision to mitigate the risk of violation of the obtained profit value it pays as a reduction in the expected profit value, while a risk-taker aggregator prefers to have a more expected profit but at the cost of more violation probability from that expected profit value. Moreover, as it can be seen in Table 1, DR aggregator participation in ancillary services and balancing markets increases its expected profit and manages its risk.

Table 1 presents the effect of participation in the different markets on the DR aggregator's expected profit. As it can be seen in Table 1, an increase in DR aggregator's risk causes a reduction in its expected profits. Moreover, the participation in the balancing and spinning reserve markets can significantly improve the profit.

The hourly offers of a DR aggregator in the mentioned markets are indicated in Fig. 2. As it can be observed, the offers of the DR aggregator to the spinning reserve market are mostly more than the one to day-ahead energy market. In other words, DR aggregator prefers to participate in ancillary services more than energy markets. In addition, the effect of participation in balancing and ancillary services markets on DR aggregator's costs and incomes are presented in Table 2.

It should be mentioned that, in this paper the response probability of DR aggregator is considered to be equal to 0.05. According to Table 2, participation of a DR aggregator in ancillary services increases its expected profit. Moreover, participation in the balancing market can also augment the profit.

Conclusion

This paper investigated the impacts of different electricity markets on the optimal behavior of a DR aggregator in a renewable-based power system. In this regard, the DR aggregator can participate in day-ahead energy, balancing and spinning reserve markets in order to maximize its profit. In addition, the uncertain nature of market prices and quantity of activated reserve by ISO were modeled using RWM method. Furthermore, CVaR was applied as a risk measure for the DR aggregator, thus being able to specify the desirable weighting between expected profit and risk due to the

Fig. 1 .

 1 Fig. 1. Electricity market scheme.

1

 1

Fig. 2 .

 2 Fig. 2. : Offers of the DR aggregator in day-ahead energy, balancing and ancillary markets.

Table 1 .

 1 Effect of different electricity markets on DR aggregator's profit and CVaR.

	Risk Level (β)	Day-ahead Energy	Electricity Market Balancing Spinning Reserve	Expected Profit ($)	CVaR ($)
				9421.5	8861.7
	0			9844.9	9420.5
				11231.1	10783.3
				9278.6	8899.0
	0.5			9676.5	9458.7
				11058.1	10897.2
				9159.4	8989.8
	1			9501.8	9492.2
				11036.4	10962.0

Table 2 .

 2 Terms of costs and incomes of DR aggregator

	Case	Participation in all day-ahead energy, balancing and ancillary services markets
	Income from day-ahead energy market	5767.3
	Income from balancing market	830.2
	Income from spinning reserve market	7565.5
	Positive imbalance income	243.3
	Negative imbalance cost	454.2
	Penalty resulted from not responding	295.1
	Cost of responsive demands	2425.9
	Expected profit ($)	11231.1

[START_REF] Shafie-Khah | A stochastic multi-layer agent-based model to study electricity market participants behavior[END_REF]

Acknowledgment. The work of M. Shafie-khah and J.P.S. Catalão was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (Ref. PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.

uncertainty of customers' behavior. The results showed that the participation in balancing and ancillary services markets can provide a significant opportunity for DR aggregators. These markets not only could increase the expected profit of DR aggregators, but also could reduce their risks.


G J Osório

J M Lujano-Rojas

J C O Matias

J P S Catalão

A Heuristic Approach for Economic Dispatch Problem in Insular Power Systems

Keywords: Insular power systems, power system reliability, probabilistic economic dispatch, wind power forecasting error

Insular power systems are characterized by their isolated geographical location, which makes their interconnection with other power systems a challenging task. Moreover, these islands have important renewable resources that allow the reduction of generation costs and greenhouse gas emissions (GHE). To guaranty the quality, flexibility and robustness of the electrical framework, the representation of renewable power forecasting error by scenario generation or even the implementation of demand response tools have been adopted. In this paper, the failure events of a specific unit are considered according to its capacity. Then, using the forced outage rate, the probability of each failure event is computed. Results of energy not supplied and fuel consumption cost are determined by applying probabilistic concepts, while the final results are obtained by fitting and evaluating a nonlinear trend line carried out using the previous results, resulting in a proficient computational tool compared with classical ones.

Introduction

Isolated geographical location and good potential to develop renewable power generation systems are two of the important topics that have driven the integration of renewable energy sources into insular power systems. In fact, in insular power systems the major problem is the high generation cost related to the type of fossil fuel used and its transportation. However, in most of the cases, there are available endogenous resources that allow reducing the generation costs and GHE. Such concerns and characteristics have motivated the development and implementation of more renewable energy sources, such as wind energy, among others depending on the island location on the globe. Notwithstanding, one of the main obstacles is related to the legislation and administrative barriers [START_REF] Colmenar-Santos | The impact of different grid regulatory scenarios on the development of renewable energy on islands: A comparative study and improvement proposals[END_REF], which can threaten the profitability of electric production.

One of the solutions that have been widely suggested is the penetration of energy storage systems (ESS) to face these problems, due to the fact that ESS can improve the flexibility of the electrical system and allow more penetration of renewable energy sources. Nonetheless, several factors such as capacity tariffs, wind potential and investment costs affect the economic viability of those projects [START_REF] Papaefthymiou | Optimum sizing of wind-pumped-storage hybrid power stations in island systems[END_REF]. Other valid way is the implementation of demand response (DR) approaches, which allows also the manipulation of a system load curve.

One other option, which is the focus of this paper, consists in introducing the uncertainty of renewable power forecasts on economic dispatch (ED) problems. For several years now, wind power forecasting error has been represented by scenario generation as a flexible approach, which enables a representation of cross-temporal characteristics of the wind power time series, influencing the spinning reserve requirements [START_REF] Wang | Wind power forecasting uncertainty and unit commitment[END_REF].

In [START_REF] Tuohy | Unit commitment for systems with significant wind penetration[END_REF], a scheduling approach based on scenario generation was proposed. In this model, several scenarios of wind power production, load demand and forced unit outages are randomly generated considering the auto-correlated nature of each time series. Then, the optimal scheduling is determined by using a mixed integer stochastic optimization algorithm, where the main objective is to minimize the expected generation cost. In [START_REF] Liu | Economic load dispatch constrained by wind power availability: a hereand-now approach[END_REF] and [START_REF] Liu | Economic load dispatch constrained by wind power availability: a wait-and-see approach[END_REF], the proposed approaches introduce the wind power generation in the ED problem as a restriction in the optimization problem. Based on the probabilistic infeasibility and using the Lagrange multiplier method, the influence of wind power behavior and penetration level on total generation cost is analyzed. In [START_REF] Hargreaves | Commitment and dispatch with uncertain wind generation by dynamic programming[END_REF], the effects of wind power generation in the ED problem are studied introducing NOx emissions modelled using an incomplete gamma function. In [START_REF] Liu | Minimum emission dispatch constrained by stochastic wind power availability and cost[END_REF], a scheduling approach is presented as a dynamic programming problem, while wind power behavior is represented as a first-order Markov process. In [START_REF] Roy | Inclusion of short duration wind variations in economic load dispatch[END_REF] an ED approach based on fact that the aggregation of wind power generation reduces its forecasting error is presented, allowing the mitigation of the stochastic relations in the optimization problem.

In [START_REF] Wu | Cost of reliability analysis based on stochastic unit commitment[END_REF], an approach incorporating load forecasting error, forced outages of generation units and transmission systems by means of a Monte Carlo simulation (MCS) is presented, enabling the estimation of the optimal reserve required in the solution of unit scheduling problems taking into account a determined reliability level. In [START_REF] Chang | Frequency-regulating reserve constrained unit commitment for an isolated power system[END_REF], a scheduling approach based on mixed-integer linear programming (MILP) to determine the optimal frequency-regulating reserve is proposed, while in [START_REF] Sahin | Allocation of hourly reserve versus demand response for security-constrained scheduling of stochastic wind energy[END_REF] another approach based on mixed-integer programming (MIP) and MCS, considering N-1 contingencies is presented. In [START_REF] Hesamzadeh | Short-run economic dispatch with mathematical modelling of the adjustment cost[END_REF], a short-run ED approach is proposed, where the different states that take place during the contingency event are analyzed and represented as a linear programming problem.

In this paper, a new approach incorporating the renewable power forecasting error and the power system reliability in the analysis of generation cost, composed of fuel consumption cost (FCC) and energy not supplied (ENS), is developed and presented. In proposed approach, the failure events of a unit or multiple units are considered according to its generation capacity. Then, using the forced outage rate (FOR), the probability of each failure event is computed. Meanwhile, a transformation is applied to the probability of each event, and therefore the sum of the probability of all considered events is one. Initial results of ENS and FCC are determined by applying probabilistic concepts, while the final results are obtained by fitting and evaluating a nonlinear trend line carried out using the initial results.

Contribution to Cloud-based Engineering Systems

Cloud-based solutions (CBS) can provide an easier and improved data connection with focus on remote tools, making suitable the concepts of the proposed study. CBS can enable a more efficient behavior of the computational resources and the information availability for the ED problem in an accurate time frame. Actually, CBS can provide an excellent interconnection to share in an efficient way the information between all electrical industry players'. A fast share of information is more and more required in the energy systems field, where data can be stored in a safe way to be ubiquitously accessed whenever needed, which is a valuable benefit of cloud-based engineering systems. Furthermore, the shared tools can improve the flexibility, accuracy and robustness of overall electrical framework, helping to improve the advent of electrical smart grids, namely in islanded location.

Insular Power System Characterization

Typically, insular power systems are characterized by a small number of conventional generation units, fewer than 26 units, and installed capacity lower than 2000 MW. Frequently, conventional generation units employed are steam turbines, combined cycle gas turbines, diesel engines, open cycle gas turbines and renewable energies [START_REF]Monthly Report[END_REF]. In most cases, such units use heavy fuel oil and/or light fuel oil, although the integration of liquefied natural gas [START_REF] Padrón | Analysis of a pumped storage system to increase the penetration level of renewable energy in isolated power systems[END_REF], [START_REF] Giatrakos | Sustainable power planning for the island of Crete[END_REF]. Moreover, the cycling of power production as a consequence of integration of stochastic renewable power sources, such as wind power, produces an increment in the number of failure events and maintenance cost. Normally, in mainland power systems, units employed to supply peak loads could have a forced out rates (FOR) of 0.02%, cycling units could have 10.15%, while base load units could have 3.69%, on average [START_REF]Distributed generation operational reliability and availability database[END_REF]. The 5-unit system under study in this paper is described in Table 1 [START_REF] Billinton | Reliability evaluation of power systems[END_REF], representing minimum and maximum output power, curve consumption parameters of units, and down/up ramps.

Moreover, units 1 and 2 are considered as peak units, unit 3 is considered as cycle unit, and units 4 and 5 are considered as base load units, representing a real small insular electrical framework. The load demand to be supplied at time t is 450 MW±10%, while the number of intervals selected for discretization was ( = 7).

Economic Dispatch Problem

The mathematical formulation of the ED problem is briefly described afterwards, where the uncertainty related with the integration of wind power forecasting (WPF) and the reliability of the power system are both incorporated in order to determine the expected value of ENS (E{ENS t }) and FCC (E{FCC t }) in a determined time instant t.

The uncertainty of WPF is directly reflected on the value of net load (L t ). FCC could be modelled according to (1):

= + ( ) + ( ) (1)
where ai, bi and ci are the coefficients of fuel consumption of each unit i, is the FCC of unit i at time t. The definition of expected generation cost is expressed in (2):

= + × ! ( 2
)
where VOLL is the value of lost load, E{•} is a function that represents the estimation of expected value of a determined variable. Also, the ED problem is constrained to the operational limitations of each unit, which typically are modelled according to their minimum and maximum power output operation, and up/down ramp constraints between the present state and previous state in time. Furthermore, it is important to note how E{GC t } is strongly dependent on FCC and ENS, so it is of the utmost importance to determine such values (E{FCC t } and E{ENS t }) in an efficient and proficient way.

Proposed Approach

The proposed approach in this paper estimates the main components of the generation cost structure (FCC and ENS). The first step determines the order in which units are going to fail. This is carried out due to the number of possible combinations of failure events that increases exponentially until 2 N . In order to avoid this problem, a limited number of units to be failed (D) is selected according to a determined criterion. In the second step, the normal operation is modeled as 1 and the failure condition as 0, so all possible combinations of failure events are estimated only considering the first-D units. This approach allows all computational efforts to be concentrated only in the failure of units of interest. In the third step, for each combination of event failure, the ED problem is solved only incorporating the forecasting error, obtaining initial values of E{ENS t } and E{FCC t }. Also, a transformation is introduced to deal with the sum of probabilities, obtaining the appropriate weights proportional to their corresponding probabilities. Then, initial values of E{ENS t } and E{FCC t } incorporating forecasting error and system reliability can be obtained by using traditional probabilistic theory. Figure 1 summarizes the proposed algorithm considering up to five points (initial values of E{ENS t } and E{FCC t }).

Introducing Forecasting Error in the Proposed Approach

Assuming that the forecasting error and (L t ) are modelled using a Gaussian PDF, such PDF is incorporated in the optimization problem by dividing it in several intervals or power classes (B) [START_REF] Zhu | Optimization of power system operation[END_REF]. A classical ED problem [START_REF] Daneshi | Mixed integer programming method to solve security constrained unit commitment with restricted operating zone limits[END_REF] could be solved for each element of the set of load values described above, obtaining the corresponding set of ENS values and FCC values.

Then, the part of E{ENS t } and E{FCC t } related to the forecasting error (E{ENS t } FE and E{FCC t } FE ) could be estimated using ( 3) and ( 4):

! #$ = (%&' ( )( ) * ( ) + ( (3)
- #$ = (. ( )( ) * ( ) + ( (4)
Fig. 1. Algorithm of proposed approach.

Introducing Forced Outage Rate in the Proposed Approach

The power system reliability is incorporated by introducing a transformation applied to the probability of occurrence of a determined number of possible failure events, allowing the substitution of the probabilities of some determined failure events by weighted values whose addition is 1. Then, the ED problem is solved according to subsection 5.1 where the expected value of ENS and FCC that corresponds to a row j considering only the forecasting error ( the values E{ENS t } and E{FCC t }, which incorporate forecasting error and system reliability considering the failures of first-D units, are obtained by using ( 5) and ( 6):

! 0 #$ and - 0 #$ )
! = ( ! 0 #$ )(1 0 ) 2 0 (5) - = ( - 0 #$ )(1 0 ) 2 0 (6)

Case Study

The 5-units case study has been presented and analyzed in order to illustrate the approach proposed in this paper, being adapted from other cases studies frequently used in the technical literature. Figure 2 shows the comparison of E{ENS t } between the results obtained from the MCS approach, considering 10,000 trials and the solution obtained from the application of probabilistic theory evaluating all possible combinations of event failures and the discretization of L t in nine intervals (B = 9), and the proposed approach for D = 1, 2,.., 5. It is possible to see how there is a small difference between MCS and the probabilistic solution due to the fact that MCS is not considering all possible combinations. Moreover, it is possible to see how the initial values of E{ENS t } converge to the probabilistic solution as the values of D tend towards N; this is the most important effect of the transformation applied. Figure 3 presents a similar comparison for the estimation of E{FCC t }, where the proposed approach has a tendency to lean towards the probabilistic solution.

In order to illustrate the approach to determine the optimal spinning reserve, the generation units presented in Table 1 have been sequentially committed according to their economic priority to supply a load demand of 60000 kW±5%. The obtained results were used later to build the curves presented in Figure 4, which describes the behavior of ENS and FCC. The behavior of E{GC t } built according to equation ( 2) is shown in Figure 5 for a VOLL between 0.2 €/kWh and 6 €/kWh.

Conclusion

The integration of renewable energy sources in insular power systems is limited by their ability to accommodate and manage the variability of such stochastic sources. Moreover, determining the appropriate amount of spinning reserve considering the uncertainty of renewable power forecasting and system reliability is of the utmost importance. Hence, this paper has incorporated in ED problem such topics in order to determine FCC and ENS values in a proficient manner, taking advantage of the tendency of initial values towards the probabilistic solution that could be provided by the CBS system. The results obtained showed how the proposed approach allows the values of E{ENS t } and E{FCC t } to be obtained with a very reasonable error in just a few seconds, compared to the solution obtained from the MCS approach.

Fig. 2 .

 2 Fig. 2. Evolution of ENS comparison betweenMCS and the proposed approach.

Fig. 3 .

 3 Fig. 3. Evolution of FCC cost comparison between MCS and the proposed approach.

Fig. 4 .

 4 Fig. 4. Behavior of ENS and FCC with the committed capacity.

Fig. 5 .

 5 Fig. 5. Generation cost for several VOLL values.

Table 1 .

 1 Main characteristic of 5 units of insular power system.n Pmin n (MW) Pmax n (MW) ai ($/h) bi ($/MWh) ci ($/MW 2 h) DRn & URn (MW/h) FOR

	1	5	50	30	27.89	0.0143	48	0.01
	2	10	50	27	19.00	0.0085	48	0.01
	3	10	70	27	18.10	0.0081	114	0.12
	4	20	120	25	12.69	0.0046	114	0.05
	5	70	200	20	9.66	0.0014	150	0.05

  are obtained. Also,

	Start
	Sorting all units according to
	their capacity, from the
	largest to the smallest
	D←1
	Built all possible
	combinations of failure
	events considering the first D
	units
	D←D+1
	Estimate the weights,
	E{ENS t } and E{FCC t }
	D≤5
	yes
	no
	Non-linear regression
	Estimation of E{ENS t } and
	E{FCC t } for D=N
	End

Table 2 .

 2 Probabilistic Priority List Results.

	Variable	MCS	Proposed	|Error|	|Error (%)|
	E{ENS} (kWh)	8810.622	9797.0216	986.3992	11.19557
	E{FCC} (€)	11603.703	11593.558	10.145	0.087432
	Time (s)	205.338	10.813	------	------

Acknowledgment. This work was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.


E M G Rodrigues

T Caramelo

T D P Mendes

R Godina

J P S Catalão
email: catalao@ubi.pt

Experimental Wireless Wattmeter for Home Energy Management Systems

Keywords: Power meter, Zigbee, MSP430, sensors, cloud computing

This paper presents a novel prototype device for domestic load energy consumption monitoring. Zigbee-based wireless connectivity is included as a basic feature of the prototype. The proposed device allows individual tracking of major energy consumption loads. Real time energy data is acquired and transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Both voltage and current measurements are implemented using Hall Effect principle based transducers, while C code is developed on two 16-bit RISC MCU. The experimental setup is described and tests are conducted in order to assess its performance.

Introduction

A growing appetite for energy is a worldwide trend observable not only on industrialized countries as well as in emerging economies. Despite being a signal of economic vitality of modern society, concerns are rising among governments on how to match the energy needs in medium and long term without pushing hard natural resources exploitation or at the expense of public debt [START_REF] Byun | A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting[END_REF]. Different levels of human activities contribute with distinguishable energy-related footprints. A large share of the world energy demand is related to domestic consumption [START_REF] Nilsson | Effects of continuous feedback on households' electricity consumption: Potentials and barriers[END_REF]. In fact, in the building sector up to 30% of consumed energy comes from residential loads, which are also responsible for 25% of demand during on-peak [START_REF] Tadokoro | Smart Building Technology [TC Spotlight[END_REF]. Refrigerators, space heating/cooling systems, water heaters, cloth washers, dryers, lighting, dishwashers, and cooking ranges in the residential sector are normally classified as major energy consumption devices [START_REF] Bozchalui | Optimal Operation of Residential Energy Hubs in Smart Grids[END_REF]. In medium term, significant energy savings may occur in the residential sector towards today's electrical networks evolution into smart grids [START_REF] Koutitas | Control of Flexible Smart Devices in the Smart Grid[END_REF]. Home located advanced metering systems will also enhance this trend by giving residential users an unprecedented freedom to organize their domestic appliances daily usage [START_REF] Georgievski | Optimizing Energy Costs for Offices Connected to the Smart Grid[END_REF]. In addition, other additional rationalization efforts can be promoted by taking advantage of available present technology.

In this context, a 2.4GHz ZigBee based distributed power metering system is proposed as part of a wireless home network area. This paper reports the development of an experimental wireless power meter device along with a wireless terminal unit to track and monitor individual energy consumption on domestic appliances. Practical development is supported on development boards equipped with microcontroller units. The power meter prototype is designed for electrical parameters measurement within AC low voltage systems. Energy related parameters calculations are root mean square (RMS) voltage or current, active and apparent power, along with load power factor.

This paper is organized as follows: Section 2 discusses the benefits of integrating home energy management services on a broad platform with cloud computing organization. Next, Section 3 presents the distributed metering system concept and unveils the wireless watt-meter prototype technical details, focusing on major engineering design steps. Afterwards, a set of experimental tests are analyzed on Section 4. Lastly, concluding remarks are made on Section 5.

Contribution to Cloud-based Engineering Systems

An Advanced Metering System (AMS) for home application relies on specialized meters designed for recording electricity and gas consumption periodically -smart meter. In turn, consumers will also access to monitors called In-Home Displays, which in turn allow them to see how much power is used at any time and how much it is costing them. The collected data will inspire consumers to use less energy, reducing their bills and supporting the environment. They will also be able to perceive when it is cheaper to run appliances [START_REF] Rahman | A Noninvasive Threat Analyzer for Advanced Metering Infrastructure in Smart Grid[END_REF]. The rising deployment of smart meters to people's homes outcomes in great quantities of data that required to be processed by power utilities. Cloud computing platforms can offer great scalability and availability concerning network bandwidth, computational resources, and storage. On the other hand, with the installation at large scale of distributed power meter devices for individualized energy consumption control purposes, an unprecedented increase of data generation arriving to smart meters is expected, which could in turn give rise to severe issues on the quality service provided by the communication infrastructure between the utility and consumers [START_REF] Green | Applications and Trends of High Performance Computing for Electric Power Systems: Focusing on Smart Grid[END_REF]. Cloud computing could alleviate smart grid agents' concerns and home owners' apprehension by providing additional dependable services. This means greater scalability and availability resources concerning network bandwidth, computational resources, and storage. Benefits of introducing two-way communications of the AMS/HAN based power meters with a cloud-based system are related to the information of the house's expected electricity usage behavior being concentrated and made available to a utility, load serving entity or an aggregator, and with those entities being able to execute their optimization processes by ensuring precise information to their consumers. Furthermore, the end-user could remotely access with a smartphone data regarding electricity consumption or even set parameters to the HAN connected domestic appliances in real-time. In addition, from the home owner standpoint, domestic informatics resources may not be adequate to store long term data. Finally, there is always a risk of losing it or been corrupted by a defective computer [START_REF] Fang | Managing smart grid information in the cloud: opportunities, model, and applications[END_REF].

Figure 1 presents an overview for a global energy management paradigm through the cloud computing link. As can be seen, the cloud computing infrastructure performs a crucial interface acting as virtual decoupler between smart grid universe and home users, while at same time offering highly interoperability concerning communication capabilities. 3 Wireless Watt-Meter Prototype

General diagram

Figure 2 shows the minimum elements for the implementation of a home energy management system based on individual wireless metering systems. Three devices comprises the system: a smart meter, a metering unit that is in charge for the power and energy readings and a terminal unit prepared to receive real time energy consumption from each one of the domestic appliances plugged to a Zigbee network operated power meter. However, since a commercial smart meter is still expensive, the concept development and experimental tests have been conducted with the metering unit and terminal unit prototypes. a) MSP430F5438A and MSP430F5529 Both models belong to MSP430 family of ultralow-power microcontrollers featuring a 16-bit RISC architecture. Major differences relay on flash and static ram memory, number of dedicated timer units, as well as serial bus interfaces (I2C, SPI and UART functionality) available. On the other hand, ADC specifications are exactly the same comprising a single module of 12-bit along with 16 channels (two are internally used) for conversion purposes. Sampling frequency is adjustable up to 200ksps in a single channel or in shared mode. For the metering unit, the MSP430F5438A MCU was chosen while the terminal unit was built on the MSP430F5529. b) CC2530 radio CC2530 radio is a mixed signal unit that comprises an IEEE 802.15.2 compliant RF transceiver along with an enhanced 8051 MCU supporting ZigBee, ZigBee PRO, and ZigBeeRF4CE standards. As the MCU side is equipped with up to 256kB of flash memory, 8kB RAM memory, including 2 USARTs, 12-Bit ADC, and 21 generalpurpose GPIO. It is well suited to cover small home areas and enables robust and flexible operation due to mesh networking capabilities offered by the Zigbee protocol. Zigbee based wireless applications are gaining increasing acceptance for smart grid and for improving HAN's based AMS functionality [START_REF] Kulkarni | Energy Monitoring in Residential Environments[END_REF], [START_REF] Namboodiri | Toward a Secure Wireless-Based Home Area Network for Metering in Smart Grids[END_REF].

c) ACS712 Current Sensor

The Allegro ACS712 current sensor is based on the Hall-effect principle discovered by Edwin Hall. According to this principle, when a current carrying conductor is placed into a magnetic field, a voltage is generated across its edges perpendicular to the directions of both the current and the magnetic field. The sensor has the following main features: low noise analog signal, output sensitivity 66-185 mV/1A (+/-20 A), adjustable bandwidth up to 80 kHz and ratiometric linear output capability.

d) LV 25-400 Voltage Sensor

For voltage sensing the choice falls on the LV 25-400 voltage transducer manufactured by LEM. It is also based on Hall Effect physical principle. It allows maximum voltage RMS readings of 400V translated on reduced current mode output, allowing easy design of the analogue interface for instrumentation systems integration. It targets applications such as AC variable speed drives, servo-motor drives, DC motor drives static converters, as well as welding equipment's power supplies.

Design Considerations and Specifications

Prototype implementation is followed by a functional block design approach, as can be seen in Figure 3. a) Scaling of analog signal levels Measures must also be appropriately handled to maximize metering system performance. Scaling is a set of procedures normally carried out to match physical signals with the ADC unit capabilities over its maximum conversion range. Therefore, the signals before being acquired have to be scaled in the correct manner. Typically, real world signals are processed through an analog signal chain. ACS712 current sensor generates a unipolar voltage as output. The radiometric feature of the ACS712 means sensor gain and offsets are proportional to the supply voltage, Vcc. Thus, for 0 A load the sensor output is equal to Vcc/2, while sensitive range is proportional to its supply voltage. For the sake of simplicity, the ACS712 device is operated from a single power supply of 2.5 V that matches MSP430f5438A ADC internal voltage reference, which in turn reduces instrumentation chain complexity. The current transfer function is given by:

= + (1)
where is the output voltage, is the gain coefficient of the transducer, is the sensed current and is the offset voltage related to the transducer zero current. Then, the ADC input voltage is expressed as:

_ = ( + ) (2)
where is the gain of the signal conditioning circuit. The transfer function of the 12-bit ADC is represented by the equation:

= _ = . _ (3)
where N !" is the 12-bit ADC code, # $%& corresponds to the ADC equal to the ADC resolution divided by the input voltage range as indicated. The same formulation must be applied. Similar formulation can be applied to describe the instrumentation chain concerning grid voltage measurement. In digital power metering devices, the analog to digital converter unit has a key role on power meter performance. When it comes to commercial electricity metering equipment's most manufacturers opt for ADC's of 24-bit to digitize grid signals. The high level of quantization steps available is sufficient to guarantee an adequate resolution and precision in electrical measurements in order to fulfill international standards such as EN 50470-1:2006 or EN 50470-3:2006 [START_REF] Holoubek | Tauglich für das Smart Grid Elektrizitätszähler auf Basis eines Cortex-M4F-Mikrocontrollers[END_REF]. However, the data acquisition capabilities available in the power meter prototype are modest in comparable terms. The MSP430f5438A has an internal ADC of lower word length configurable between 8-bit and 12-bit in steps of 2-bit. The resolution of the ADC determines the dynamic range of measured voltage and current. For voltage measurement, a maximum RMS reading of 300V is considered. Then, the voltage peak-to-peak range is given by:

' ()* *() = +,, * √ * = /0/ (4)
Considering that the accuracy of the MSP430F5438A's 12 bit ADC is 12 , which is equivalent to 0.02% of the full scale ADC range, theoretical resolution is respectively:

' 3:(5 ) (5 ) = /0/ * ,, ,,, = ,. 7 (5)
' 3:89 = +,, * ,, ,,, = ,. ,:

b) Antialiasing filter requirements

For digitizing analog samples the minimum sampling rate f < must be more than twice the signal maximum frequency, according to Shannon's sampling theorem. On the contrary, if the rule is not respected the analog signal can't be fully reconstructed from the input samples, posing an undesired effect know as aliasing. In view of the MSP430f5438A is not tailored for signal processing-intensive calculations, a performance and accuracy trade-off was decided. Thus, for power measurements the acquired electric quantities should be limited with a harmonic content up to 20th order. In practical terms, the prototype bandwidth is 1000Hz concerning a fundamental grid frequency of 50Hz. Therefore, a low pass filter has been designed to limit voltage and current signals bandwidth. A Bessel filter was chosen due to its good amplitude and transient behavior performance. In addition, the filter's group delay is approximately constant across the entire pass-band, which is a mandatory design specification to limit distortion impacts on filtered signals. A cut-frequency of twice of the signal bandwidth specified can be observed from Bessel filter frequency response in Figure 4. This is required to guarantee that the filter output attenuation at 1000Hz is virtually zero. High order Bessel filter and, consequently, higher sampling frequency selected have to do also with the ADC dynamic range, which in turn is related with its signal-to-noise ratio (SNR) [START_REF] Devices | Fundamentals of Sampled Data Systems AN-282 Application Note[END_REF]. For our study, it is assumed that the MSP430f5438A's 12-bit ADC is ideal. Hence, the SNR is 74 dB. Having this in mind, the Nyquist Frequency (f = /2) is approximately 12.3 kHz. In order to comply with Shannon's theorem, the sampling rate has been selected slightly above the minimum recommended. c) DC offset removal Before moving to the energy transit related parameters calculation, the offset term must be eliminated from incoming signals. Instead of simply subtracting a fixed offset from the signals, a high pass filter was developed on Matlab. The first order IIR (Infinite Impulse Response) filter has a -3 dB cutoff frequency of 0.1Hz and sampling frequency of 25000s/sec.

The obtained digital filter can be expressed as:

@ABC = 0.996 * @AB -1C + 0.996 * IABC -0.996 * IAB -1C

Taking the z-transfom of both sides and rearranging the terms, gives:

J(K) = ,.LL:*,.LL:K M *,.LL:K M (8)
To minimize the effects of binary word length on filter's outcome performance, especially when extracting the offset term on low voltage and current signals, filter's coefficients as well the result are handled in 32-bit arithmetic fixed point.

Experimental Tests and Results

A set of tests have been performed to verify the experimental watt-meter (Figure 5) implemented algorithms performance. For this purpose, a conventional electric heater has been chosen since it is a common domestic appliance. The domestic load offers two power regulation levels. The user can select between half and full power rated at 2000W. The tests focused on comparing current waveform acquisition by the device developed with the wave trace observed on an oscilloscope. In addition, a current clamp was employed to determine current RMS values at half and at full power. These measures made it possible to assess the prototype RMS estimation proximity to current clamp readings.

Figures 6 and7 show that the current acquisition is relatively noisy; however, RMS estimation is not compromised.

In fact, on both load regimes the calculated values are very much in agreement within the current meter readings. Naturally the discrepancy found on prototype reading has to be confirmed with more tests covering a wider group of electric loads. This will be a future work of the authors. Moreover, a calibrated meter tool is mandatory to proceed to a metrological formal verification.

Conclusions

This paper presented a prototype device that allows individual tracking of major energy consumption loads. Energy data was acquired in real-time and transmitted through the RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Both current and voltage measurements were executed using Hall Effect principle based transducers, while C code was developed and loaded on two 16-bit RISC MCU. The experimental assembly was described and a set of tests were carried out in order to validate the prototype developed.

Fig. 1 .

 1 Fig.1. Cloud services interoperability with AMS/HAN based home energy management system.

Fig. 2 .

 2 Fig.2. Wireless power meter and terminal unit block diagram.

Fig. 3 .

 3 Fig.3. Sensing, acquisition and post-filtering block diagram.

Fig. 4 .

 4 Fig.4. 10th order Bessel filter frequency response: a) Magnitude and phase; b) Group delay.

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig.6. Half power operating electric space heater: a) Prototype acquired current profile; b) ACS712 output.

E.M.G. Rodrigues et al.

Acknowledgment

This work was supported by FEDER funds (European Union) through COMPETE and by Portuguese funds through FCT, under Projects FCOMP-01-0124-FEDER-020282 (Ref. PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048