Organic Architecture for Small- to Large-Scale ... - IEEE Xplore

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 11, NOVEMBER 2009

Organic Architecture for Small- to Large-Scale Photovoltaic Power Stations Cuauhtemoc Rodriguez, Member, IEEE, and Justin D. K. Bishop

Abstract—Increased widespread deployment of power generation from photovoltaics is consistent with binding agreements to reduce carbon emissions and increase the penetration of electricity from renewables and political aspirations to increase security of energy supply. However, in order for these generation facilities to compete in increasingly open power markets, they must be low cost and provide high-quality and high-quantity outputs. The organic architecture suggested in this paper proposes a solution that provides these advantages, using modular-power-electronic and energy-storage components, to facilitate scalable plants, from kilowatt to megawatt size. Specifically, the inclusion of power– conversion building blocks (PCBBs), grid-interactive power units (GPUs), and power-system control units allow efficient transfer of power from the point of energy conversion to the point of common coupling. A specific example of a 24-kW plant illustrates that, through optimum switching of PCBBs, the GPU can transfer 95.46% of the daily available energy to the transmission grid. Index Terms—Photovoltaic (PV) power systems, power generation, reconfigurable architectures.

I. I NTRODUCTION

T

HIS PAPER explores the concept of flexible renewableenergy power stations. Based on organic structures, a flexible power station can be sized from a few kilowatts to several hundred megawatts. There is firm evidence of the negative impacts that large-scale emissions of carbon from fuel combustion are having on the climate and global ecosystem at large. In 2006, 66.90% of the 18.93-TW · h electricity generated worldwide originated from combustion of carbon-emitting fuels [1]. Concurrently, there are agreements that require the reduction of total carbon emissions, most notably the Kyoto Protocol to the United Nations Framework Convention on Climate Change [2]. This agreement requires Annex I countries to reduce their greenhouse-gas emissions by at least 5% below 1990 levels in the period of 2008–2012. Additionally, regions and nations have enacted legislation to mitigate emissions from specific sectors, an example of which is the Directive 2001/77 of the European Commission [3]. This law requires 22.10% electricity from renewables by 2010. An advantage of increasing reliance on renewables, as indigenous sources, is the decreased dependence on imported fuels. Therefore, there are environmental, legal, and security-of-supply bases for Manuscript received December 28, 2008; revised May 14, 2009. First published June 5, 2009; current version published October 9, 2009. C. Rodriguez is with Cambridge Consultants Ltd., CB4 0DW Cambridge, U.K. (e-mail: [email protected]). J. D. K. Bishop was with the Department of Engineering, University of Cambridge, CB2 1PZ Cambridge, U.K. He is now with the Institute for Carbon and Energy Reduction in Transport, University of Oxford, OX1 3QY Oxford, U.K. (e-mail: [email protected]). Digital Object Identifier 10.1109/TIE.2009.2023642

increasing the penetration of electricity from renewables in a fuel mix. However, in many developed countries, the vertically integrated structure of the electricity sector is being replaced by a multiplayer environment, following an open-market paradigm. Therefore, for renewable power plants to compete in such markets, their output must be predictable, stable, and of a high quality. In this sense, the incorporation of energy storage and advanced forecasting methods have been shown to improve the confidence of renewable-plant operators to bid for a power contract and minimize the mismatch between demand and supply at the time of delivery [3], [5], [6]. The state of energy storage technologies and future trends are presented in [7]. As with conventional thermal power plants, the issue of sizing must be addressed. However, for renewable plants, sizing considerations go beyond only satisfying power demand to include space and resource constraints. To accomplish this, the work proposed utilizes an organic-system approach. Organic systems are fundamental in the evolution of living beings. Such a structure allows the gradual and consistent growth of an organism while maximizing the resources and yield. A successful organic system consists of entities with well-defined functions that respond to needs of higher rank. Furthermore, entities can grow or decrease in number or be replaced by alternatives to accommodate the adaptive nature of the system whose ultimate goal is to survive and evolve. Translated to power systems, an organic architecture would allow the growth or contraction of the system where entities are generators, compensators, protection devices, control rooms, and transmission and distribution lines. The same paradigm can be applied to renewable-energy power stations, given that energy sources have much lower power rating. As such, the organic scheme is fulfilled due to the existence of systems within systems where the ultimate goal is to satisfy power generation and demand, i.e., to survive. Fig. 1 shows the organic structure of a renewable-energy power station. The prime mover consists of several—one to thousands of—energy sources, for example, photovoltaic (PV), wind, hydrogen, etc., that inject energy into a transmission network. Separate entities regulate the levels in the transmission line(s) and feed that energy into the power-system transmission or distribution grid. The system, from generation to transmission, is designed in a way to enable its expansion or contraction. Moreover, due to its organic nature, it has self-healing capabilities. This is implemented in the form of redundancy in the generators, transmission lines, and power-injection and control blocks. For similar reasons, installations can range from a single generator,

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RODRIGUEZ AND BISHOP: ORGANIC ARCHITECTURE FOR PHOTOVOLTAIC POWER STATIONS

Fig. 1.

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Organic renewable-energy power station.

for example, 10 kW, to several thousands, such as 100 MW. Differences in installation size will be explored next. II. I NSTALLATION S IZING Organic power architectures offer the benefit of scalability. The following sections illustrate the components required for different installation sizes in terms of their power rating. For site renewable-energy-source sizing, the reader is referred to the work presented in [8]. A. Multimegawatt Installations A possible organic architecture of a multimegawatt renewable-energy power station is shown in Fig. 1. Power rating for this type of installation can range from 0.10 to 1000 MW: It may be impractical in terms of land extension to have large power stations. An example of such a large PV plant is the 5.2-MW installation in Kameyama, Japan [9]. On the left side of Fig. 1, energy sources are depicted as small circles. These sources can have power ratings of a few kilowatts, for example, PV arrays, micro-wind turbines, microhydro generators, fuel cells, etc. Each source has a power converter and protection devices (small boxes) that condition the energy and transform it to direct current (dc). These converters also perform tasks such as mechanical tracking for solar PV systems and electrical maximum-power-point tracking. Examples of mechanicaltracking systems can be found in [10] and [11]. Energy is transferred to the power inverters from the source using a low-voltage (LV, 400–1000 V) dc interconnection net-

work. To increase robustness, a station may have several generator clusters with interconnection lines that can be routed and/or bypassed so that a failure in one cluster or line does not result in complete plant unavailability. Hence, lines are interconnected through switches and/or circuit breakers (small boxes). Each generation cluster, comprising many generators and their respective power conditioners, connects to a gridinteractive power unit (GPU, vertical rectangles). This unit is made of a processing unit that performs control tasks and an array of power-conversion building blocks (PCBBs, horizontal rectangles). The latter converts dc to an alternating current (ac) that is compatible with the electricity grid. Each PCBB has a low power rating, such as 1–5 kW. Thus, an array of tens to hundreds is needed to match the generation cluster, which can be rated up to 100 kW. Power-electronic building blocks have been reported in the literature; for example, ABB uses a common platform for their rail motor drives and large wind inverters [12]. Depending on the power-generation level, the GPU can connect or disconnect PCBBs to maximize system efficiency and energy quality. Furthermore, it also controls the voltage level at the dc interconnection network and the amount of power that each PCBB injects to the grid. GPUs include communication features, wired or wireless, that enable data transfer with each energy source. This characteristic permits the disconnection and/or monitoring of individual generators. AC power from the GPUs is injected into the LV side of stepup transformers. Depending on the power rating of the station, another transformer can be used to step up from medium

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Fig. 2. Multikilowatt installation.

voltage (MV, 10–50 kV) to high voltage (HV, >100 kV). In such large power stations, a higher rank power-station control unit (PSCU) is used. This unit oversees the behavior of each GPU and coordinates efforts for high performance and reliable operation of the power station. If required, it can also regulate real and reactive power by controlling real- and reactive-power compensators. Real-power compensation can be implemented as battery banks or flywheels; this method of compensation was widely explored in the past for wind farms and PV systems (see, for example, [13]–[15]). Reactive-power compensators can be passive—capacitor and reactor banks—or active (synchronous compensators). As an example, consider a 100-MW peak solar PV park. Each generator source can have 50 panels that are rated 200 W for a total of 10 kW. These panels can be mounted into a structure with mechanical tracking and will have its own powerconversion unit and protection circuit. Then, a generation cluster of 100 kW can be made up of ten sources connected to one dc interconnection line and one GPU with 50 PCBBs (each PCBB is rated 2 kW). To complete the power station, 1000 generation clusters are needed. Each of the 1000 GPUs connects to one LV-to-MV transformer, and all the MV outputs are connected to one MV-to-HV transformer. In summary, the major blocks are as follows: 1) one 100-MV · A MV-to-HV transformer; 2) 1000 100-kW LV-to-MV transformers; 3) 1000 GPUs; 4) 50 000 PCBBs; 5) 10 000 energy-source power converters with protection; 6) 500 000 solar panels. The capacity of the converters and transformers would need to have some margin with respect to the maximum source rating, but that was not taken into account. The land area needed for such a power station can be up to 1.5 km2 . B. Multikilowatt Installations Multikilowatt installations consist of a single cluster of energy sources with power rating of up to 100 kW, as shown in Fig. 2. In this type of installation, only one GPU and one LV-to-MV transformer are used. Since each GPU can handle a limited amount of energy, up to 100 kW, for example, the maximum size of the plant is limited to the rating of the GPU. However, by adding more clusters, the station can be scaled up to a multimegawatt system. Due to the control of the GPU over energy sources and PCBBs, this installation can be expanded and contracted without major architecture changes. In particular, for PV instal-

lations, the modular decentralized approach may overcome partial-shading effects [10], [16]. C. Kilowatt Installations Using the same blocks, a smaller system of one or two energy sources can be designed. In such a case, the GPU would only contain a handful of PCBBs. III. E NERGY-S OURCE P OWER -C ONVERSION U NIT PV cells are semiconductor devices that release electrons when illuminated. Therefore, their source impedance is exponential and varies with temperature and irradiance level. When evenly and constantly illuminated, they produce dc current at a voltage level that is equivalent to the semiconductor bandgap of 0.5–0.7 V. A solar panel is made of several cells, typically connected in series. Most standard panels have voltage ratings of up to 45 V and current of up to 8 A. For large installations, several panels would be connected in series and parallel. For example, a 10-kW array can comprise 50 panels connected in five parallel strings of ten panels with the following characteristics: 1) maximum voltage: 450 V; 2) minimum voltage: 240 V; 3) maximum current: 30 A; 4) typical maximum-power-point voltage: 370 V; 5) typical maximum-power-point current: 27 A. An electronic maximum-power-point-tracking controller is used to force the electric operating point around the array’s maximum efficiency point. Furthermore, the 50 panels can be mounted on a fixed frame or in a tracking structure. It is documented that tracking structures can yield 40% more energy than fixed ones [17]. In the latter case, a mechanical-tracking controller is needed to position the array surface normal to sunrays. In mechanical-tracking systems, a global-positioning-system device can be used to get a coarse positioning of the array. The controller will then fine-tune the position for optimum yield. Power is transferred from the array to the dc interconnection network. For a dc network of, for example, 650 V, and given that maximum power occurs below this voltage, a boost converter is a suitable solution (see, for example, [18]). A possible solution is shown in Fig. 3. The controller regulates the output power based on the maximum power available from the PV source and the maximum-power-point algorithm (not shown). Protection includes short-circuit current, overcurrent, overvoltage, and overtemperature. The converter can be disconnected from the array and/or from the dc interconnection using

RODRIGUEZ AND BISHOP: ORGANIC ARCHITECTURE FOR PHOTOVOLTAIC POWER STATIONS

Fig. 3.

PV power converter, filter, protection, and isolator.

Fig. 4.

Power station of 200 kW (normal operation).

relays and switches. A detailed operation description of the boost converter is beyond the scope of this paper, which focuses on the overall architecture and inverter efficiency and reliability. IV. I NTERCONNECTION S YSTEMS Each generating source connects to the dc interconnection system or network where power is transferred to the GPU. The GPU regulates the voltage in the network, and consequently, each source can take it as a stiff voltage source. A generation cluster is formed by many sources connected to the interconnection network and to a single GPU. Therefore, the powertransfer capability of the network must be greater than the GPU. Moreover, enhanced system reliability is achieved by routing power to alternate networks in the case of a GPU failure. This operation is illustrated by means of an example. Consider as an example a 200-kW power station formed by two generation clusters, as shown in Fig. 4. Each cluster is formed by ten 10-kW sources connected to a dc interconnection line and to a GPU. The GPU contains 50 3-kW PCBBs that inject power to the LV side of a 150-kV · A transformer. A PSCU interacts with each GPU. Each interconnection line is rated to 150 kW. In normal operation, the circuit breaker to the left is open (clear box), and all other circuit breakers are closed (dark

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boxes). Each generation cluster generates up to 100 kW that is transferred to the MV electricity network. In the event of a GPU failure, power is routed through an adjacent line. However, knowing beforehand that each line can carry 150 kW, only five sources can be rerouted, as shown in Fig. 5. The solution mentioned earlier requires parts of the system to be overrated. However, this measure may not be necessary, given the variability of some resources, for example, solar and wind. With some built-in intelligence, GPUs and the PSCU can connect or disconnect individual energy sources so that ratings are never exceeded. For example, if one GPU is out of service and generators are producing only 50% of their rated power, then all the 20 generators can be routed through the second GPU. As soon as generators produce more power, for example, 60%, then the GPU and/or PSCU can disconnect four sources. In this way, the availability of the plant is high, and yield is maximized with respect to investment cost. Each interconnect line used in the aforementioned example can be specified as follows: 1) 2) 3) 4)

nominal voltage: 650 V; overvoltage trip point: 750 V; maximum current rating: 300 A; minimum wire diameter per pole: 11 mm.

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Fig. 5. Power station of 200 kW (fault operation).

Fig. 6. PCBB power-electronic topology.

Wires are preferably buried and only emerge above ground to connect individual sources using an interconnect panel. In this way, a cluster can be increased by cascading new sources without laying out wire for the whole station. Circuit breakers and/or switches are used to route power and protect the station against overload conditions. The trip current for the aforesaid example can be set at 350 A. A. Plant Communication Network For the self-healing feature described previously to operate reliably, energy sources, GPUs, and isolation breakers need to form a communication network in conjunction with the PSCU. In this way, when a fault occurs in a GPU, the PSCU is able to overtake control of energy sources and to reallocate units to adjacent GPUs. The 200-kW-station example described before would require approximately 26 network nodes. Note that the specific implementation of communication network is irrelevant, provided that it can cover the required land area, offers enough real-time acquisition and control, and is cost effective. The work presented in [19] provides a thorough look at power-system communications that are directly applicable to the organic architecture presented here. V. PCBB PCBBs are the cornerstone of the organic power-station architecture. Each of these blocks, under control of the GPU, transforms power from the dc interconnection network to ac power, which is fed to the LV side of the grid transformer. Given that each block has low power rating, for example, 3 kW,

a large power station requires thousands of units. Two main advantages emerge from this: 1) PCBBs can be manufactured in large volume, and hence, their unit cost will be small [20], and 2) power stations can be of any size, rating from several kilowatts to several megawatts. Other advantages are as follows: 1) ability to connect and disconnect modules, depending on resource availability; 2) balance power among units for improved power quality and efficiency; 3) high reliability and redundancy; power can be rerouted if a unit fails [10], [20]. On the other hand, the disadvantage is that it requires external control from the GPU. The tasks of each PCBB are as follows. 1) Convert dc power from the interconnection network to dc power synchronized with the grid and with high quality. 2) Feed the amount of power indicated by the GPU. 3) Disconnect from the grid when indicated by the GPU. A. PCBB Power-Electronic Topology Inverters for PV power systems have been proposed in the literature for many years, and many companies now offer a wide range in power rating and input voltages (see, for example, [21]–[23]). An example topology of a PCBB is shown in Fig. 6. It consists of a three-legged converter connected to the three-phase mains supply. A three-phase topology is preferred to generate a more uniform power flow from energy sources to the grid network. Each module would therefore contribute with three-phase balanced operation. Furthermore, a single

RODRIGUEZ AND BISHOP: ORGANIC ARCHITECTURE FOR PHOTOVOLTAIC POWER STATIONS

Fig. 7.

PCBB three-phase currents.

Fig. 8.

PCBB current-control diagram.

three-phase inverter requires fewer components than three one-phase inverters do. Power injection is controlled using current-mode control, as explained in Section V-B. The magnitude, however, is regulated by the GPU. Each PCBB leg is synchronized to its respective phase, and they all inject the same amount of power and at unity power factor. Fig. 7 shows the three phase currents at a power level of 3 kW, where the dc input voltage is set to 650 V and the lineto-neutral mains voltage is 250 Vrms. The inverter operates in three-phase balanced conditions, and in the event of a singleline fault, the entire unit is shut down in order to prevent unwanted negative- and zero-sequence currents. From the previous discussion, it is clear that the minimum rating for a PV installation is the size of the PCBB design, i.e., in this example, 3 kW. A modular power station can grow in multiples of this factor. B. PCBB Control The GPU has control of the power-injection level at all times, and it monitors the dc interconnection point and the three-phase mains. Hence, the PCBB only needs to control the current at each of the phases. That is achieved by means of a current control circuit, whose simplified diagram for phase a is shown in Fig. 8. A mains voltage sensor is used to generate a synchronization signal that enables the PCBB to synchronize each phase current.

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A digital signal processor also generates reference currents with a magnitude based on the information relayed from the GPU. The reference current ia_ref is compared against the actual output current ia to take a switching decision. During the positive half cycle of the phase voltage, only the upper switch of a leg is actuated. Conversely, during the negative half cycle of the phase voltage, only the lower switch of a leg is actuated. On the positive half cycle, turning on the upper switch increases the current, while turning off the upper switch reduces the current. On the negative half cycle, turning on the lower switch decreases the current, while turning off the switch increases the current. Switching frequency is set by the clocks applied to the Set (S) and Reset (R) inputs of the RS flip-flops. This implementation forces the switch to be on and off at every switching period, thereby simplifying filter design. Fig. 9 shows the time diagram of the clock signals and phase currents at both positive and negative half cycles. At time t0 , switch Q1 is on, and the current ia is increasing. At time t1 , the signal CLK_R generates a reset pulse that turns transistor Q1 off, and the current drops. At time t2 , the signal CLK_S generates a set pulse that turns transistor Q1 on, and the current increases again. At time t4 , the current is greater than the reference, and the flip-flop is reset, causing Q1 to turn off and the current to drop. At time t5 , a reset pulse is generated but has no effect because the transistor is already off. At time t6 , Q1 is set again, and the process repeats. During the negative half cycle, turning Q2 on causes the current to drop, as

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The GPU regulates the voltage on the dc interlink and instructs each PCBB on its connection status and the amount of current to inject. It disconnects all PCBBs when the mains are abnormal, monitors power and energy generation, and provides a user interface for manual control and supervision. It also contains a power supply to power its own electronics and all the PCBBs. This supply is driven from the mains, and it has a backup battery with monitor and controller for continued safe operation during outages. Note that the battery is not to store energy from the sources; it is intended for power-supply support for supervisory functions. A. GPU Architecture Fig. 9. PCBB current-control time diagram. TABLE I PCBB THD AND E FFICIENCY

illustrated in time instances t9 and t13 . A reset pulse or an active comparison will turn Q2 off, and the current increases. Table I shows the total harmonic distortion (THD) and efficiency of the PCBB in percentage at different power levels. Results were obtained through SPICE simulations, where transistors Q1 −Q6 are SPA11N60C, inductors La −Lc are 20 mH, capacitors Ca −Cc are 470 nF, and Cdc is 2 mF. The switching frequency was selected at 100 kHz. A similar efficiency profile has been reported for PV inverters [24], and the International Energy Agency Photovoltaic Power Systems Programme reports an average peak efficiency of 94% in 2005 across the 331 systems under study [25]. In the latter work, a higher efficiency of 98.5% is achieved by using more advance silicon carbide transistors. C. PCBB Interface Each PCBB communicates with the GPU to determine its status: enabled or disabled. Moreover, the GPU sets the magnitude of the current that each PCBB injects to the mains. Communication is made using a differential and isolated bus using a standard protocol, for example, RS422, RS485, MODBUS, FIELDBUS, PROFIBUS, or CAN. Each PCBB has a distinct identifier used for communication with the GPU. Identifiers are predetermined based on their connection inside a GPU. In this way, the GPU knows where each PCBB is connected, its status, and the amount of power that it is injecting. VI. GPU A GPU is a unit comprising several PCBBs and circuitry for monitoring the dc interconnection network, the mains connection, and communication links with energy sources connected to its dc network and with the PSCU.

A simplified GPU architecture is shown in Fig. 10. Each block will be described next. 1) Supervisory Processor: This processor is in charge of managing the GPU. It monitors the grid voltage and current and calculates real- and reactive-power flows, power factor, THD, and grid impedance. In the event of loss of mains or abnormal conditions, it instructs the GPU to halt power generation. The supervisory processor also gathers information from the generation sources through a wireless link. Apart from monitoring their voltage, current, and status, it can instruct them to halt generation. 2) Power Processor: This processor monitors the dc interconnection point. Based on voltage control, it calculates the cumulative power injection of all PCBBs and instructs each of them to generate a specific amount. It can also disable or enable individual PCBBs so that they normally operate at maximum efficiency. This is done in order to maximize the overall system efficiency and current injection quality. Clearly, if the energy sources are generating maximum power, then most or all PCBBs will be converting power close to the limit rating. The power processor communicates with each PCBB through a differential bus. It addresses each of them using a unique identifying method. 3) Interface Processor: This processor manages the user interfaces, such as a keyboard and a display. A user can browse for system status and display information on the screen. The interface processor also communicates with the PSCU (if any) for higher order commands, such as shutdown or rerouting of power. 4) Power Supply: This unit takes power from the mains and regulates voltage rails needed for the operation of the GPU. A backup battery is used to keep the processors in the GPU powered in the event of loss of mains. B. GPU Energy Control A simplified control diagram of GPU energy (voltage) control is shown in Fig. 11. The controller can have the form of a proportional–integral–derivative with negative gain. Controller G(s) increases the power injection if the voltage in the dc network is greater than the reference level and conversely. The processor calculates the number of units that must be enabled and the injection level for each based on an optimization algorithm. The goal is to operate the system at its

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Fig. 10. GPU architecture.

Fig. 11. GPU power-processor control diagram.

(6)

the total number of PCBBs in the GPU; Pdemand is the total power demand taken from the power-processor controller of Fig. 11; f (Pi ) is the efficiency curve of each PCBB taken from Table I; Ploss is the total system power loss; and Pi_ max is the maximum rating of each PCBB. The optimization problem defined by (1)–(6) is of a mixed-integer and nonlinear type, where the integer variables are ui and can only take the value of 0 or 1. A description of the solver algorithm can be found in [26]. The results of the optimization problem, the values of Pi , are sent to the PCBBs. Consider the following example: 1) input solar insolation over 15-min intervals for 24 h1 (see Fig. 12); 2) maximum power generation = 24 kW; 3) GPU containing nine units, rated 3 kW each.

where P ∈ Rn and u ∈ I n are the vectors of PCBB power injections Pi and their binary variables ui , respectively; n is

1 Data are that of March 21, 2007, accessed from the University of Waterloo Weather Station (http:// weather.uwaterloo.ca/data.html).

maximum efficiency. Therefore, the mathematical problem is formulated as follows: min {Ploss (P )}

(1)

u i Pi

(2)

ui [(1 − ηi )Pi ]

(3)

ηi = f (Pi ) ∀ i = 1, . . . , n 0 ≤ Pi ≤ Pi_ max ∀ i = 1, . . . , n  1, if unit i is enabled ui ∀ i = 1, . . . , n 0, if unit i is disabled

(4) (5)

P,u

subject to Pdemand = Ploss (P ) =

n  i=1 n  i=1

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Fig. 12. Input solar power (normalized to 24 kWp). Fig. 14. Efficiencies of (solid line) modular and (broken line) single-inverter architectures.

Fig. 15. Total PCBB output power using (solid line) modular and (broken line) single-inverter architectures.

Fig. 13. Number of active PCBBs.

Fig. 13 shows the number of active PCBBs at a particular 15-min interval during a sunny day. It is seen that more units are gradually included in order to maximize system efficiency. Fig. 14 shows the overall system efficiency of a modular architecture and of single-inverter architecture. The singleinverter architecture consists of one 24-kW inverter with the same efficiency curve used for individual PCBBs. Comparing the two sets of data and calculating system efficiency as ηsystem =

Daily Energy Output Daily Energy Input

(7)

yield 95.46% and 91.26% for the modular and single-inverter architectures, respectively (Fig. 15). In this example, 15-min windows were used. In practice, it may be necessary to update Pdemand more frequently. Since optimization algorithms are computationally exhausting, a pragmatic approach would be to calculate the solutions offline and store them in the memory of the GPU. In this case, a new solution can be obtained and forwarded to PCBBs in a matter of seconds.

C. Current Loops in Parallel PCBBs PCBBs in a GPU share the same dc-link and three-phase mains connections, and current could potentially flow from one inverter to another according to Kirchhoff’s current and voltage laws. This is a well-known problem in parallel inverters, and there are solutions to tackle it (see, for example, [27]–[30]). Moreover, incorporation of suitable LCL, current-loop control, and current-sharing synchronization can yield high-quality parallel current sharing across inverter stacks, permitting a modular design approach for inverters [31]. D. Reliability and Probability of Failure The modular approach of the architecture presented here offers higher reliability and, therefore, lower probability of failure on account of the system parallel configuration. Typically, systems are characterized by their mean time to failure (MTTF) [32]. This figure can be obtained theoretically by analyzing the MTTF of each individual component and subsystem or through accelerated lifetime tests or even field tests. With this information, we can calculate the system reliability for a given

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Fig. 16. PSCU.

power demand. Consider, for example, that each inverter in the 24-kW power plant illustrated earlier in Section VI has an MTTF of 15.94 years, i.e., 7158 failures in 1×109 h (FIT) [33]. The probability of a unit failing within one year is defined as   hours in one year P (t < one year) = F IT . (8) 1 × 109 Therefore, for the inverter unit of the example, the probability of failure is P (t < one year) = 0.0627. A complete system failure requires all PCBBs to fail, or, put another way, the system functions when at least one PCBB is operating, albeit with peak power derating. Thus, the failure of the eight-PCBB parallel configuration is given by the logical AND of each inverter, assuming that each PCBB fails independently of the others [34]. Consequently, the 24-kW system comprising eight inverters will have a probability of failure of (0.0627)8 within a year (neglecting other components in the system for simplicity since they can be designed to be more robust due to lower electrical and thermal stress). Now, we build further redundancy in the form of an additional inverter so that the total is nine, but the system is still rated at 24 kW. The probability of failure of the 24-kW system within a year is now two units failing. Assuming that the units are independent, we get P (t < one year)new = [P (t < one year)]2 = 0.0039. Note, however, that the system MTTF is not changed. If we calculate the probability of failure in 15 years, we would obtain 0.94 for eight inverters and 0.88 for nine inverters. Nonetheless, redundancy has added robustness and plant availability in the early and middle life of the system. VII. PSCU A PSCU is needed in large-scale power plants. The reason behind this is the cumulative impact that renewable generation has on the operation of the electricity network. Moreover, realand reactive-power compensation may be required to stabilize power flows and guarantee reliable power delivery. A simplified block diagram of the control unit is shown in Fig. 16. A brief description of the blocks will be provided next.

This section is included for completeness of the power-station architecture. It is beyond the scope of this paper to delve into the details of each block. A. Station Computing Center This mainframe computer processes all the information needed for the operation of the power station. From the control room, an operator can visualize the status of every GPU and energy source, protection device, and power flows in the dc interconnection network. In addition, power can be rerouted to alternative GPUs or dc lines in the event of partial failures. From the computing center, real and reactive power can be regulated. Reactive-power compensation can be in the form of capacitor and reactor banks or in the form of static synchronous compensators. If battery banks or flywheels are available or needed, then real power can be stored or injected to stabilize power flows. B. SCADA and Communications to Power Operation Center As with any power station, communications and supervisory control and data acquisition (SCADA) are required with the power operation center. Data are acquired via remote terminal units spread throughout the system, while supervisory control permits the remote control of all circuit breakers on the system [35]. Operators can monitor the status of the plant and issue instructions about real- and reactive-power compensation. It is also important that, in the event of blackouts, power is restored in an organized manner to avoid grid collapse. C. Protection Supervisory Unit This unit is responsible for coordinated protection around the power station. Protection is composed of circuit breakers, surge arresters, etc. D. VDUs and Control Inputs These units are the man–machine interfaces for operation of the power station. A user can see data in the various visual

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display units (VDUs) and input commands with keyboards, dials, buttons, etc. E. Isolated Communication Drivers to GPUs All GPUs can be monitored and controlled from the PSCU through this block. F. Reactive- and Real-Power Compensation In power stations, reactive-power compensation is achieved by controlling the excitation in the synchronous generators. In a renewable-energy plant, that capability is not available, and thus, other measures need to be taken. Capacitor and reactor banks are frequently used in substations to regulate reactivepower flows and voltages. Alternatively, power electronics can be used to provide a continuous reactive-power resource. The latter implementation is normally referred to as static synchronous compensation. In particular, providing reactivepower support to PV power plants reduces the likelihood that the generators will be disconnected following a fault, improves voltage restoration, and improves system reliability [36]. With real-power support, such as from battery banks or flywheels, power can be stored or released to the network to optimize power flows. For example, during peak periods, more power can be injected to the system, and during light interval, energy can be stored. Full advantages of energy storage and their control have been reported in the literature (see, for example, [37]–[39]). VIII. C ONCLUSION The proposed organic power-station architecture illustrates how variable-output renewable-fuelled power generators may be successfully incorporated into an existing power system. Such a proposal supports binding legislation on carbonemission reductions and increases the penetration of renewablepower generators. Moreover, it supports aspirations to achieve greater energy security. The organic approach of the plant architecture permits installations ranging from kilowatts to megawatts, depending on location and demand, while its modular composition facilitates such expansion at lowest cost and highest efficiency. In the case of generation from PV, the PCBB and GPU components ensure maximum-power-point operation and highefficiency current inversion, while the PSCU and energy storage provide power-factor correction and output smoothing at the point of common coupling. An example 24-kW power station, using up to nine 3-kW PCBB blocks, uses the GPU to optimize the number of active PCBBs to transfer power to the grid transmission network. The result is an overall transfer efficiency of 95.46% compared to 91.26% of the monolithic approach, due to the PCBBs operating within their most efficient power range. ACKNOWLEDGMENT The authors would like to thank the University of Waterloo Weather Station administrators for the use of their data in this paper.

R EFERENCES [1] International Energy Agency (I.E.A.), Key World Energy Statistics 2008, pp. 26–28, 2008. [2] United Nations (U.N.), Kyoto Protocol to the United Nations Framework Convention on Climate Change, 1997. Article 3. [3] European Commission (E.C.), Directive 2001/77/EC on the Promotion of Electricity Produced From Renewable Energy Sources in the Internal Electricity Market, p. 202001. [4] P. Pinson, C. Chevallier, and G. N. Kariniotakis, “Trading wind generation from short-term probabilistic forecasts of wind power,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1148–1156, Aug. 2007. [5] G. N. Bathurst and G. Strbac, “Value of combining energy storage and wind in short-term energy and balancing markets,” Elect. Power Syst. Res., vol. 67, no. 1, pp. 1–8, Oct. 2003. [6] S. K. Kim, J. H. Jeon, C. H. Cho, J. B. Ahn, and S. H. Kwon, “Dynamic modeling and control of a grid-connected hybrid generation system with versatile power transfer,” IEEE Trans. Ind. Electron., vol. 55, no. 4, pp. 1677–1688, Apr. 2008. [7] J. M. Carrasco, L. G. Franquelo, J. T. Bialasiewicz, E. Galvan, R. C. P. Guisado, M. A. M. Prats, J. I. Leon, and N. Moreno, “Powerelectronic systems for the grid integration of renewable energy sources: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1016, Jun. 2006. [8] T. Markvart and L. Castaner, Practical Handbook of Photovoltaics: Fundamentals and Applications. Oxford, U.K.: Elsevier Sci., 2003. [9] K. Odagaki, “Practical study on 5.2 MW PV system in Sharp’s Kameyama plant,” in Proc. 4th IEEE Power Convers. Conf., Nagoya, Japan, Apr. 2007, vol. 1, pp. 1212–1216. [10] G. Petrone, G. Spagnuolo, R. Teodorescu, M. Veerachary, and M. Vitelli, “Reliability issues in photovoltaic power processing systems,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2569–2580, Jul. 2008. [11] N. Fermia, G. Lisi, G. Petrone, G. Spagnuolo, and M. Vitelli, “Distributed maximum power point tracking of photovoltaic arrays: Novel approach and system analysis,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2610– 2621, Jul. 2008. [12] ABB, ABB Review—The Corporate Technical Journal of the ABB Group, Mar. 2008. [13] G. O. Cimuca, C. Saudemont, B. Robyns, and M. M. Radulescu, “Control and performance evaluation of a flywheel energy-storage system associated to a variable-speed wind generator,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1074–1085, Aug. 2006. [14] H. Akagi and H. Sato, “Control and performance of a doubly-fed induction machine intended for a flywheel energy storage system,” IEEE Trans. Power Electron., vol. 17, no. 1, pp. 109–116, Jan. 2002. [15] S. J. Chiang, C. M. Liaw, W. C. Chang, and W. Y. Chang, “Multimodule parallel small battery energy storage system,” IEEE Trans. Energy Convers., vol. 11, no. 1, pp. 146–154, Mar. 1996. [16] W. Xiao, N. Ozog, and W. G. Dunford, “Topology study of photovoltaic interface for maximum power point tracking,” IEEE Trans. Ind. Electron., vol. 54, no. 3, pp. 1696–1704, Jun. 2007. [17] International Energy Agency (I.E.A.), Trends in Photovoltaic Applications: Survey Report of Selected IEA Countries Between 1992 and 2007, p. 27, 2008. [18] N. D. Benavides and P. L. Chapman, “Boost converter with a reconfigurable inductor,” in Proc. IEEE Power Electron. Spec. Conf., Orlando, FL, Jun. 2007, vol. 1, pp. 1695–1700. [19] M. Shahidehpour and Y. Wang, Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing. New York: Wiley-IEEE Press, 2003. [20] Y. Chen and K. M. Smedley, “One-cycle-controlled three-phase gridconnected inverters and their parallel operation,” IEEE Trans. Ind. Appl., vol. 44, no. 2, pp. 663–671, Mar./Apr. 2008. [21] R. González, J. López, P. Sanchis, and L. Marroyo, “Transformerless inverter for single-phase photovoltaic systems,” IEEE Trans. Power Syst., vol. 22, no. 2, pp. 693–697, Mar. 2007. [22] J. Rodriguez, J.-S. Lai, and F. Z. Peng, “Multilevel inverters: A survey of topologies, controls, and applications,” IEEE Trans. Ind. Electron., vol. 49, no. 4, pp. 724–738, Aug. 2002. [23] T. Kerekes, T. Teodorescu, C. Klumpner, M. Sumner, D. Floricau, and P. Rodriguez, “Evaluation of three-phase transformerless photovoltaic inverter topologies,” in Proc. Eur. Conf. Power Electron. Appl., Aalborg, Denmark, Sep. 2007, pp. 1–10. [24] B. Burger, B. Goeldi, D. Kranzer, and H. Schmidt, “98.5% inverter efficiency with SiC-MOSFETs,” in Proc. 23rd Eur. Photovoltaic Conf. Exhib., Valencia, Spain, Sep. 2008. 4EP.1.15.

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[25] International Energy Agency (I.E.A.), Cost and Performance Trends in Grid-Connected Photovoltaic Systems and Case Studies, p. 13, Dec. 2007. 16–17. [26] M. Tawarmalani and N. V. Sahinidis, Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming. Dordrecht, The Netherlands: Kluwer, 2002. [27] H. Cai, R. Zhao, and H. Yang, “Study on ideal operation status of parallel inverters,” IEEE Trans. Power Electron., vol. 23, no. 6, pp. 2964–2970, Nov. 2008. [28] L. Chen, L. Xiao, C. Gong, and Y. Yan, “Circulating current’s characteristics analysis and the control strategy of parallel system based on double close-loop controlled VSI,” in Proc. IEEE Power Electron. Spec. Conf., Jun. 2004, vol. 6, pp. 4791–4797. [29] J. F. Chen and C. L. Chu, “Combination voltage-controlled and currentcontrolled PWM inverters for UPS parallel operation,” IEEE Trans. Power Electron., vol. 10, no. 5, pp. 547–559, Sep. 1995. [30] L. Xiaozhu and Z. Rongbo, “Efficient parallel control scheme of single phase inverters,” in Proc. Chin. Control Conf., Jun. 2007, vol. 1, pp. 204–208. [31] C.-L. Chen, J.-S. Lai, Y.-B. Wang, S.-Y. Park, and H. Miwa, “Design and control for LCL-based inverters with both grid-tie and standalone parallel operations,” in Conf. Rec. IEEE IAS Annu. Meeting, Edmonton, AB, Canada, Oct. 2008, pp. 1–7. [32] A. L. Julian and G. Oriti, “A comparison of redundant inverter topologies to improve voltage source inverter reliability,” IEEE Trans. Ind. Appl., vol. 43, no. 5, pp. 1371–1379, Sep. 2007. [33] C. Rodriguez and G. A. J. Amaratunga, “Long-lifetime power inverter for photovoltaic ac modules,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2593–2601, Jul. 2008. [34] G. J. Anders, Probability Concepts in Electric Power Systems. New York: Wiley-Interscience, 1990. [35] M. E. El-Hawary, in Electrical Energy Systems, London, U.K., 2000, pp. 301–303. [36] Y. T. Tan and D. S. Kirschen, “Impact on the power system of a large penetration of photovoltaic generation,” in Proc. IEEE Power Eng. Soc. Gen. Meeting, Tampa, FL, Jun. 2007, vol. 1, pp. 1–8. [37] J. M. Carrasco, L. G. Franquelo, J. T. Bialasiewicz, E. Galvan, R. C. P. Guisado, M. A. M. Prats, J. I. Leon, and N. M. Alfonso, “Powerelectronic systems for the grid integration of renewable energy sources: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1017, Aug. 2006.

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[38] R. B. Schainker, “Executive overview: Energy storage options for a sustainable energy future,” in Proc. IEEE Power Eng. Soc. Gen. Meeting, Denver, CO, Jun. 2004, vol. 2, pp. 2309–2314. [39] S. Voller, A. R. Al-Awaad, and J. F. Verstege, “Benefits of energy storages for wind power trading,” in Proc. IEEE Int. Conf. Sustainable Energy Technol., Singapore, Nov. 2008, vol. 1, pp. 702–706.

Cuauhtemoc Rodriguez (S’96–M’04) received the B.S. degree (with honors) in electronics engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico, in 2000, the M.Eng. degree in electrical engineering from McGill University, Montreal, QC, Canada, in 2003, with his research focusing on power-system stability and load-flow analysis, and the Ph.D. degree from the University of Cambridge, Cambridge, U.K., in 2006, where his main interest was in the interconnection of photovoltaic sources to the electric grid. In 2006, he was a Power Electronics Design Engineer with Enecsys Ltd., where he designed solar inverters. Since 2007, he has been with Cambridge Consultants Ltd., Cambridge, U.K., as a Power Electronics and Control Engineer.

Justin D. K. Bishop received the Ph.D. degree from the Department of Engineering, University of Cambridge, Cambridge, U.K., in 2009, with his research focusing on sustainable-electricity-system design. In particular, this research investigated notions of sustainable consumption and electric-powergeneration fuel mixes that are cointegrated and optimized with transmission network planning and best practice system deployment. He is currently a James Martin Research Fellow with the Institute for Carbon and Energy Reduction in Transport, University of Oxford, Oxford, U.K.