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High penetration of renewables in the distribution network brings significant uncertainties, especially during volatile weather conditions. Hence, the network controllers should be designed to account for these uncertainties, and respond to unpredictable events like voltage-dips for reliable voltage control. This paper proposes a control scheme, where inverter-based Distributed Energy Resources (DERs) respond locally with Q(V) control and adapt to set-points assigned by the centralized controller (CC). The Robust Constrained Model Predictive Control (RCMPC) scheme is proposed for centralized voltage control. It robustly deploys control resources from DERs and tap-changers to regulate the lower/upper bound of node voltages within the targeted limit. Moreover, RCMPC ensures minimum resource utilization by relaxing the targeted voltage limit whenever it anticipates significant uncertainties. The CC is implemented in Python, which communicates with the RMS model of the UKGDS network for measurements and dispatching set-points. The performance of RCMPC is compared with deterministic MPC (DMCP) at 5, 10, and 15-minute time-steps of CC. The proposed RCMPC can regulate the node voltage even at a higher degree of uncertainty seen at a 15-minute time-step. In contrast, the DMPC could not contain the node voltages under the targeted limit and worsened at a larger time-step.

Photovoltaic (PV) generation systems, unlike conventional generators, do not possess any inherent inertia. The problem is further amplified by the fact that they are non‐dispatchable and highly intermittent by nature. Furthermore, the rate at which PV power output changes is of a much shorter timescale than that of conventional generators. Thus, integrating them in large quantities to the power grid without appropriate measures will compromise the power grid stability. One of the feasible solutions is to implement the concept of virtual inertia to PV systems, by adding energy storage systems (ESS) such as batteries in parallel with PV systems. In this paper, the ESS will be controlled to regulate the ramp rate (RR) of the PV power output to the grid, which smoothens the overall generation power profile. By modelling the grid, PV, ESS and load individually in the s‐domain and using an equivalent overall two‐machine, two‐area model, the impact of implementing virtual inertia is demonstrated. The results highlight the effectiveness in using ESS to control the RR and improving the transient responses of the overall grid system. Moreover, the economic impact and feasibility of ESS are also analysed using a residential PV and load profile from Singapore.

Voltage sensitivity matrices are fundamental for the model-based control of the distribution networks. Here, an accurate estimation of voltage sensitivity to active/reactive power injections and tap-position of an on-load tap changer is essential for network modelling. In literature, voltage sensitivity to tap-position is computed by assuming its equivalence with voltage sensitivity to voltage magnitude of the slack bus. However, this approach provides an approximate estimation, and it leads to significant error when the external grid has a low strength. Hence, this study proposes an Enhanced Z-bus method, which comprises of analytical expressions for direct estimation of the voltage sensitivity to tap-position and active/reactive power injections. Importantly, the enhanced Z-bus method can accurately compute the voltage sensitivity to tap-position and active/reactive power injections for any strength of the external grid. The proposed method is tested in a radial (UKGDS), mesh (Case33bw) and reconfigurable (MV Oberrhein) network. Furthermore, it is benchmarked with the perturb-and-observe, Jacobian method and the proprietary methods of DigSILENT PowerFactory. Finally, the proposed method is found to be computationally competent with the existing Jacobian and Z-bus methods.

Plug-in Electric Vehicles will be proliferating in many urban societies around the world due to aggressive targets set-up by many Governments around the world. This trend will get a boost especially after the COVID-19 lockdown phase, when it was evident that Green House Gases in the atmosphere could be brought down by large extent in a very short-time. However, many urban areas are densely populated and many car owners do not even have a proper car park. In such case, how will they get an access to charging facility. In this paper, we present a model of an aggregated charging facility that is located as a work-place car park. The arrival and departure of PEVs are modeled using the office statistics. We show how this random variable can be represented as an electrical load to the grid. We then show charging strategies that can be used to ensure the PEVs have a full charge at the time of departure. This is to eliminate the need for residential charging as there may not be such a facility available. The charging of PEVs is carried out for minimize the loading on the grid and yet meet the constraints of the PEV users.

In this paper, smart charging strategies incorporating a unified grid-to-vehicle and vehicle-to-grid charging framework are proposed for optimal integration of plug-in electric vehicles (PEVs) within the existing distribution system infrastructure. Two smart strategies with objective functions considering minimization of total daily cost and peak-to-average ratio, respectively, are developed to study the impact on PEV charging from an economic and technical perspective. The proposed strategies are implemented for PEV charging at workplace car parks located in a 37-bus distribution system and an analytical study is presented to evaluate the maximum possible PEV penetration that the existing distribution infrastructure can accommodate corresponding to the two strategies. A comparative analysis of the two strategies is performed in terms of various economic and technical benefits that are derived. Moreover, a performance comparison of the two strategies in presence of slow and fast charging of PEVs is also presented. Finally, an investigative study is conducted for both the strategies to evaluate the maximum PEV penetration that can be integrated in the upcoming years without infrastructure reinforcement. The simulation results present a comprehensive evaluation of the two proposed strategies.

Q(U) control for PV inverter has become the mandatory local voltage controller to comply with the grid connection code at Medium Voltage level CLC-FprTS 505492. The parameters of Q(U) control is optimized day ahead for which the tap operation is not generally taken into account. As a result, the optimal dead-band of Q(U) controller could be larger than the dead-band of On Load Tap Changer, which leads to an increased number of tap operation during intermittent irradiation day. Considering this problem, the paper proposes the MPC based centralized controller to assist the local control for modifying their Q(U) characteristics primarily to suppress the number of tap operation and also participate all the inverter for tighter voltage regulation. The proposed method is implemented in a test distribution network modeled in DigSilent. Finally, the effectiveness of the method is shown using RMS simulation method.

The paper draws attention towards the significance of positive sequence modeling of AC drive systems for creating the dynamic load model of industrial facilities or for determining the transient stability of the industrial network. The DigSILENT power factory has prebuilt features for positive sequence modeling which are utilized while building and testing the control of AC drives. The power interruption ride-through control has been developed for such a model which prevents the tripping of the AC drives by regulating the DC link voltage at a safe level by utilizing the stored kinetic energy of rotating mass. The simulation results show that such a mechanism helps to ride-through even 1-second power interruption. Finally, the amount of investment saved is estimated while adopting this mechanism over the capacity extension of energy storage for 1-second power interruption ride-through.

The Photovoltaic (PV) sources have become the most popular renewable energy resources as it is modular and are deployed quickly. However, the distributed PVs are highly volatile and can have high ramping characteristics where the ramping can go up to 90% of its capacity in about 20 s due to events such as the passing of fast-moving clouds. This kind of PV ramps with high magnitudes can lead to voltage instability in distribution networks that are usually dominated by Induction motor (IM) loads. This paper explains the mechanism of such an instability analytically using Q-V analysis with distribution feeder and IM load characteristics. Highlighting the gradual replacement of IM loads by Variable Speed Drive (VSD) loads due to energy efficiency policies, the paper demonstrates the enhanced voltage stability under VSD loads by both time simulation and analytical approach. Furthermore, the analysis is extended to a realistic distribution network namely, United Kingdom General Distribution System (UKGDS) and extensive case studies are conducted to analyze the voltage stability during PV ramping events at various load compositions of IMs and VSDs. Moreover, the minimum VSD penetration level required to avert the voltage instability at various PV penetration levels have also been determined.

In a Ship-based power system, depending on the type of ship and its operation, load profile could be very dynamic. The dynamic load demand is met by the use of multiple diesel-generators running in parallel with higher than normal ramping. These generators are typically oversized to meet the highly dynamic load demand. During the period of low demand, generators are operated on a suboptimal loading point. This leads to increased fuel consumption and higher mechanical stress on the generators which makes the operation inefficient. Using an energy storage system as a buffer allows operation of generators in their cost-efficient point making the overall operation cost and energy efficient. This paper addresses the selection of type and size of the energy storage system for a ship electrical power system. Firstly, the optimal operating strategy based on a multiobjective cost function for a different combination of storage systems have been formulated. Secondly, the best-suited energy storage system are determined by comparing parameters like the fuel consumption, capital cost, replacement cost and size for a target operational life of 5 years. Results show that an energy storage system not only reduces the fuel consumption but also restricts the operation of generators beyond their normal operating limits.

The Variable Speed Drives (VSD) are anticipated to replace the Induction Motors (IM) by 50%, in 2020, in developed cities of Europe [1]. Therefore, it is essential to incorporate VSD loads while analyzing voltage stability studies in the distribution network with high PV penetration. The collective load response from VSD loads tend to slow the rate of change of node voltages of distribution system, during the PV ramping events. However, this dynamic load characteristic of VSD is dependent on its capacitor size. Meanwhile, there is trend for reducing the capacitor size in VSD load at equipment level. Thus, this paper investigates the impact of reducing DC capacitor size in VSD load, on voltage stability of distribution system during PV ramping events. For the investigation, the paper involves United Kingdom General Distribution System (UKGDS) where load is modeled with combination of static load, IM, and VSD. It is determined that the maximum PV penetration reduces from 80% to 50% when the capacitor size in VSD is lowered from $360\mu F/kW$ to $25\mu F/kW$ respectively. Furthermore, the increased VSD penetration could delay and even avert the voltage collapse from PV ramping events during grid contingency condition. It is revealed that the minimum VSD penetration required to avert instability during such condition, even at 100% PV penetration, increases from 50% to 65% when the capacitor size of VSD is reduced from $360\mu F/kW$ to $25\mu F/kW$ respectively.

Renewable energy sources (RESs), such as wind and solar power become an integral part of the energy mix in the vast majority of countries around the world. Due to the variable and uncertain nature of RESs, the power flow of power systems with renewable penetration is no longer deterministic but probabilistic. Power flow study is vital to system daily operations as well as stability and economics. This paper investigates the effects of renewable energy integration on power system from a stochastic power flow point of view. Electrical load is assumed to follow a normal distribution. Wind and solar power scenarios are generated from the Weibull and lognormal probability distribution functions. The modified IEEE 39 bus testing system with RES penetration is modeled in a professional power system simulation software named DIgSILENT PowerFactory and it is further used for testing. Optimal power flow is running in each scenario of the stochastic process and the system without RES penetration is regarded as the base case. The effects of RESs on power system are analyzed comparatively in terms of generation cost, power losses, bus voltage deviations and line loading.

An important consideration for the design of micro- grid for an uninterruptible infrastructure is the ability to ensure its continual operation regardless of the health of the utility grid. This paper proposes a multiple layer power security design concept for re-designing of such an uninterruptible electrical network with an improved reliability and sustainability by integrating with renewable resources and energy storage systems. The re-designed microgrid network and the control scheme were modelled and simulated in PowerFactory. Potential problems during the network re-design such as protection discrimination and power factor degradation due to high PV penetration have been analysed and solutions have been provided. System performance of the re-designed network has been evaluated through various test cases.

Conventional energy planning focused on energy cost, GHG emission and renewable contribution based on future energy demand, fuel price, etc. Uncertainty in the projected variables such as energy demand, volatile fuel price and evolution of renewable technologies will influence the cost of energy when projected over a period of 15–30 years. Inaccurate projected variables could affect energy security and lead to the risk of high energy cost, high emission and low energy security. The energy security is an ability of generation capacity to meet the future energy demand. In order to minimize the risks, a generic methodology is presented to determine an optimal energy mix for a period of around 15 years. The proposed optimal energy mix is a right combination of energy sources that minimize the risk caused due to future uncertainties related to the energy sources. The proposed methodology uses stochastic optimization to address future uncertainties over a planning horizon and minimize the variations in the desired performance criteria such as energy security and costs. The developed methodology is validated using a case study for a South East Asian region with diverse fuel sources consists of wind, solar, geothermal, coal, biomass and natural gas, etc. The derived optimal energy mix decision outperformed the conventional energy planning by remaining stable and feasible against 79% of future energy demand scenarios at the expense of 0–10% increase in the energy cost. Including the nuclear option in the energy mix resulted 26.7% reduction in the total energy cost, 53.2% reduction in the GHG emission and guarantees feasibility against 79% of future energy demand scenarios over a 15 year planning horizon.

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

We have developed a technique using the matrix pencil method (MPM), which was augmented with an ellipse fitting algorithm, that can classify three-phase voltage dips only using subcycle voltage data. This technique uses the MPM to extract the fundamental frequency components and reconstructs the fundamental voltage space vector that often changes into an ellipse during a fault. This ellipse's parameters are then estimated by an ellipse fitting algorithm to classify the dip. We have demonstrated that this technique can differentiate between highly distorted and similar voltage dips only using a quarter-cycle of data, which is equivalent to 5 ms in a 50-Hz alternating-current system. This method is further improved by prefiltering and downsampling the data to reduce the computation time and is implemented on a National Instruments CompactRIO platform. This system classified the dip in real time at about 10–12 ms after fault detection when tested with reproduced fault voltage waveforms amplified to a voltage level of 415 V. Our method thus executes faster than the conventional method of the fast Fourier transform that requires at least a full fundamental cycle time of 20 ms. This technique serves as a method of analyzing subcycle voltage dip phenomena and can be potentially extended to other fast subcycle electrical events.

In addition to random power fluctuation, the unpredictable high ramp rate is another uncertainty of Photovoltaic (PV) power output. It increases the requirement of both the amount and response speed of operation reserves at different time scale in the grid. The fast fluctuation of PV needs to be compensated by frequent ramp up and down reserves. Traditional generators will face heavy wear and tear to provide such support. While energy storage system (ESS) can provide fast bidirectional power support, ESS' response time and duration differs for various technology and material, and are suitable to provide reserve at different time scale. In this paper, a multiple ESS solution is proposed to suppress the PV ramp rate at different time scale. An optimal coordinated control strategy is developed to minimize the operation cost of the multiple storage system. Instead of suppressing ramp rate at individual time scale, the proposed control strategy coordinates multiple storage to control ramp rate at different time scale in one problem. In this way, the contribution of one time scale's ramp rate control to other time scale's ramp rate is considered. Using the coordinated control strategy, the required ESS capacity can be reduced. It can also minimize the PV and ESS system operation cost subject to grid regulation on ramp rate. The control algorithm has been demonstrated using a triple-input ESS example and its effectiveness have been studied. The results show the effectiveness of the proposed control in regulating ramp rate at different time scale.

Energy storage is needed in micro-grid to help solve the problem of intermittency introduced by renewable energy sources, enhance power quality and improve controllability of power flow. This paper presents an energy manager for energy storage system (ESS) in micro-grids. The objectives of the energy manager are focused on improving the energy efficiency and extending the life expectancy of ESS while ensuring constraints of energy storage modules are complied with. To this end a smart local prediction and local scheduling algorithm is proposed. A battery lifetime model that uses the proposed Peukert lifetime energy throughput based on the workload of the battery is developed. Verification shows that in the long run, the energy manger can improve overall energy efficiency of ESS from 74.1% to 85.5%, and improve estimated lifetime of 2 Battery Packs in ESS from 3.6 years and 2.4 years to 5 years and 5.7 years respectively.

As Photovoltaic (PV) penetration level rapidly increases in the modern power systems, the adoption of energy storage systems (ESS) is a preferred option to control the rapid and unpredictable PV power fluctuations. It is desired to develop new reliability evaluation methods that can model and evaluate the reliability impact of these new entries. This paper has proposed an area risk based method that can take into account the PV intermittence and ESS operations when evaluating the unit commitment risk (UCR). The rapid changes of PV power is accounted for every 5-minute interval during the lead time. A control algorithm for the ESS is also developed and integrated into the evaluation of UCR. The method is applied to quantify the effects of different ESS capacities on the system's load carrying capability. System planners can utilize this method to determine the proper ESS installation for a system with given PV penetration level. This method is also useful for system operators to decide on the required committed units as well as the charging and discharging power of ESS given PV fluctuation.

Battery Energy Storage Systems (BESS) have gained extensive application in both grid and microgrid applications. One major type of BESS are electrochemical batteries such as Lead-Acid and Lithium-Ion batteries which have limited number of lifecycles. The common way of considering their operation cost is using a constant value such as LCOE (levelized cost of energy). However, as shown herein, given the same amount of energy output, the battery lifecycle degradation, and thus the degradation cost, can vary at different operation conditions (voltage, current, power, state of charge (SOC)) by up to 6 times. Herein a model for the dynamic battery operation cost as a function of its dispatch power and SOC is developed. The model also considers the dependency of battery voltage on its current and SOC, which equivalently takes into account the dependency of its conversion efficiency on its power and SOC. Preliminary simulations demonstrate that using the proposed model, instead of the LCOE, for Microgrid operation optimization microgrid operation cost is lower by up to 12%.

With increasing PV power penetration in the modern power grid, a cost-effective solution to address PV intermittency becomes more and more compelling. The ramp rate of PV power can reach 60% of its rated capacity in just 30 seconds. Energy storage is a technically feasible solution to suppress the adverse impacts of injecting intermittent power output with such a high ramp rate into the grid, but its cost is very high. Therefore, to reduce the system cost of integrating PV and maximize the grid operation profit, optimal sizing of energy storage is necessary. In this paper, a method of optimizing energy storage size for controlling PV ramp rate is presented. The characteristics of PV ramp rate are first investigated. Based on the results, an energy dispatch model for controlling PV ramp rate with fast response energy storage is developed. The optimal size of energy storage which minimizes system operation cost to accommodate high PV penetration is subsequently determined.

As the penetration of renewable energy resources proliferate, inverters have been employed as interface in distributed power systems. To achieve expanded power level and system redundancy, parallel connection of inverters has been widely used. This paper derives mathematical models to quantitatively evaluate the reliability of parallel inverters under different topologies and control strategies. A framework to determine the number of inverters in parallel in terms of reliability and cost optimization is proposed. Sensitivity analysis is carried out to identify the impact of parameter variation on system reliability and total cost. At last, power densities of different structures are analyzed for a fair comparison.

This paper presents a multiagent system (MAS) for real-time operation of a microgrid. The proposed operational strategy is mainly focused on generation scheduling and demand side management. In generation scheduling, schedule coordinator agent executes a two-stage scheduling: day-ahead and real-time scheduling. The day-ahead scheduling finds out hourly power settings of distributed energy resources (DERs) from a day-ahead energy market. The real-time scheduling updates the power settings of the distributed energy resources by considering the results of the day-ahead scheduling and feedback from real-time operation of the microgrid in real-time digital simulator (RTDS). A demand side management agent performs load shifting before the day-ahead scheduling, and does load curtailing in real-time whenever it is necessary and possible. The distributed multiagent model proposed in this paper provides a common communication interface for all components of the microgrid to interact with one another for autonomous intelligent control actions. Furthermore, the multiagent system maximizes the power production of local distributed generators, minimizes the operational cost of the microgrid, and optimizes the power exchange between the main power grid and the microgrid subject to system constraints and constraints of distributed energy resources. Outcome of simulation studies demonstrates the effectiveness of the proposed multiagent approach for real-time operation of a microgrid.

Renewable-energy-based microgrids are a better way of utilizing renewable power and reduce the usage of fossil fuels. Usage of energy storage becomes mandatory when such microgrids are used to supply quality power to the loads. Microgrids have two modes of operation, namely, grid-connected and islanding modes. During islanding mode, the main responsibility of the storage is to perform energy balance. During grid-connected mode, the goal is to prevent propagation of the renewable source intermittency and load fluctuations to the grid. Energy storage of a single type cannot perform all these jobs efficiently in a renewable powered microgrid. The intermittent nature of renewable energy sources like photovoltaic (PV) demands usage of storage with high energy density. At the same time, quick fluctuation of load demands storage with high power density. This paper proposes a composite energy storage system (CESS) that contains both high energy density storage battery and high power density storage ultracapacitor to meet the aforementioned requirements. The proposed power converter configuration and the energy management scheme can actively distribute the power demand among the different energy storages. Results are presented to show the feasibility of the proposed scheme.

This paper proposes a multi-agent system for energy resource scheduling of an islanded power system with distributed resources, which consists of integrated microgrids and lumped loads. Distributed intelligent multi-agent technology is applied to make the power system more reliable, efficient and capable of exploiting and integrating alternative sources of energy. The algorithm behind the proposed energy resource scheduling has three stages. The first stage is to schedule each microgrid individually to satisfy its internal demand. The next stage involves finding the best possible bids for exporting power to the network and compete in a whole sale energy market. The final stage is to reschedule each microgrid individually to satisfy the total demand, which is the addition of internal demand and the demand from the results of the whole sale energy market simulation. The simulation results of a power system with distributed resources comprising three microgrids and five lumped loads show that the proposed multi-agent system allows efficient management of micro-sources with minimum operational cost. The case studies demonstrate that the system is successfully monitored, controlled and operated by means of the developed multi-agent system.

This paper proposes a dynamic electrothermal model that can be simulated with the power electronic circuit simulator. It includes a temperature-dependent loss calculation of power semiconductor devices. The proposed model is used to estimate the transient junction temperature of the semiconductor devices. In so doing, the resulting junction temperature is used to facilitate the power sharing between parallel-connected converters. The use of power-cycling method based on junction temperature helps in increasing the over all system efficiency and reliability of the system. This part of the paper focuses on the method of thermal modeling and discusses how the model can be used for different converters, modulation techniques, and devices.

A key feature of microgrid concept is the ability to operate in islanding mode. Under such operating scenario, the distributed generators within microgrid should form a high-quality voltage supply for the load to sustain its normal operation. Power converters, the interface of renewable sources and energy storage systems, are important components of microgrids. The size and the number of the power converters required will depend on the fluctuating power flow within the microgrid. In this paper, we propose a hybrid control architecture to balance the power shared among the multiple interfacing inverters and optimize the system-operating efficiency. Simulation and real-time test results are provided to validate the proposed hybrid control scheme.

A simple and accurate fuel-cell model is required for fuel-cell-based power-electronic applications. An artificial neural network (ANN) model is developed in this paper to model some nonlinear structures within the hybrid model of a proton-exchange-membrane fuel-cell stack. It improves accuracy and allows the model to adapt itself to operating conditions. Moreover, the temperature effect on the fuel-cell stack is represented as the current effect by using ANN to help estimate the relationship between current and temperature. The real-time implementation of the proposed ANN model is realized via a dSPACE system. Experimental results are provided to verify the validity of the proposed model.

An interleaved current-fed full-bridge converter has the capability to step up the voltage while maintaining a low input current ripple. Therefore, it is suitable for application such as a front-end converter for fuel cell where the source current ripple has to be small. However, since the source voltage varies with change in load profile, it is a challenge to design a stable controller that works well for a wide operating range. In this paper, an energy-based approach using a Brayton-Moser modeled passivity-based controller is proposed along with an augmented integrator to achieve voltage regulation under wide operating range. Experimental results verify that the proposed controller is able to achieve good dynamic performance and stable operation under wide operating range.

In order to actively control the power flow between the load and ultracapacitor, a dual-active-bridge converter can be utilized. Conventional phase-shift modulation (PSM) has difficulties in working with a wide range of source voltage levels and load power levels. In addition, a traditional PI controller does not provide satisfactory behavior as the relation between the control variable and output voltage is nonlinear. In this paper, a hybrid modulation scheme is proposed to extend the power range of operation from 100% to 16.67% instead of 100% to 58.33% that is achieved by conventional PSM. A feedback-linearized controller is also designed to achieve a better dynamic response under various load conditions. The details of the modulation principle and controller design are given, and the experimental results verify the proposed modulation and control scheme.

The slew rate of the inductor current is limited by the inductance value and the voltage across the inductor. In a buck converter, when the controller is saturated, the voltage across the inductor during a step-up load transient is V in-V out, while during a step-down load transient, it is -V out. Thus, a buck converter with a large conversion ratio offers asymmetrical step-up and step-down transients. Since the rate of fall of the inductor current is much slower than the rate of rise of the inductor current, the step-down transient lasts longer than the step-up transient for the same change in the load current. The step-down slew rate can be increased by reducing the inductance, but it results in higher inductor current ripple, and hence, higher losses in the power converters. In this paper, we present a novel topology for improving the step-down load transients without reducing the inductance value. The scheme operates only during load transients and restores to the normal operating conditions during steady-state operation. It provides reduced voltage overshoots and faster settling times in output voltage during such transients. The proposed scheme is tested on a 1-V/12-A buck converter switching at 1 MHz, and the experimental results are presented.

Current sensing is one of the important functions in a dc-dc converter. It may be used for implementing current mode control or for achieving current sharing in a paralleled converters. Many current sensing schemes have been reported in literature, but they are either lossy or they require precise knowledge of the component value. In this paper, a giant magneto resistive effect based sensor is used for low voltage, high current voltage regulator module type applications. Since the current sensing is based on magnetic field, the sensing accuracy depends upon the placement of sensor on the conductor. Optimum distance is obtained for sensing the current with desired accuracy. It also gives the optimum location of the sensor to minimize the interference from closely lying current carrying conductors. Finally the performance of the sensor is experimentally verified on a 1 V/15 A buck converter switching at 1 MHz.

Multilevel inverters can reduce the common mode voltage generated. Schemes have been reported for multilevel inverters that reduce the common mode voltage. However, most of the schemes result in reduced modulation depth, high switching losses, and high harmonic distortion. This paper proposes a space-vector modulation scheme to reduce common mode voltage for cascaded multilevel inverters. The proposed scheme can increase the voltage range of operation by about 17% and can produce lower total harmonic distortion than the previously proposed schemes. The scheme is explained for five-level inverter. The scheme can be easily extended to a n-level inverter. Both experimental and simulation results are provided.

An interleaved current-fed full bridge (ICFFB) dc-dc converter is proposed in this paper that has low input current ripple to meet the fuel cell demands. By interleaving two isolated CFFB converters with parallel input and series output connection, both input current ripple and output voltage ripple can be reduced. In addition, the size of the magnetic components and current stress of the semiconductor devices on the input side are also reduced. Similarly, smaller voltage rating components can be used on the output side. Only one digital signal processor microcontroller is used to generate phase-shifted gate signals and to implement a cascaded digital control system. The main features of the proposed converter are high efficiency, small passive component size, and small input current ripple. Experimental results for a 1.2-kW interleaved CFFB converter are provided in the paper

Multilevel inverters are being increasingly used for high-power medium-voltage applications. Three-level neutral point clamped (NPC) topology is most widely used topology of multilevel inverters. However, at higher modulation index, particularly in over-modulation region, the neutral point fluctuation deteriorates the performance of the inverter. Furthermore, operating the inverter at lower modulation index implies that it is operated at lower voltages only, and the installed DC link capacity is not fully utilized. This paper proposes a simple space vector PWM scheme for operating a three-level NPC inverter at higher modulation indices including over-modulation region, with neutral point balancing. Experimental results are provided.

This paper describes the Experimental Power Grid Centre (EPGC) microgrid test facility, which was developed to enable research, development and testing for a wide range of distributed generation and microgrid technologies. The EPGC microgrid facility comprises a integrated electrical and thermal grid with a flexible and configurable architecture, and includes various distributed energy resources and emulators, such as generators, renewable, energy storage technologies and programmable load banks. The integrated thermal grid provides an opportunity to harness waste heat produced by the generators for combined heat, power and cooling applications, and support research in optimization of combined electrical-thermal systems. Several case studies are presented to demonstrate the testing of different control and operation strategies for storage systems in grid-connected and islanded microgrids. One of the case studies also demonstrates an integrated thermal grid to convert waste heat to useful energy, which thus far resulted in a higher combined energy efficiency. Experiment results confirm that the facility enables testing and evaluation of grid technologies and practical problems that may not be apparent in a computer simulated environment.

Electricity markets in countries around the world are being restructured in pursuit of economic efficiency through competition. However, unpredictability in electricity prices and previous occurrences of market failure have indicated a need to better understand the complex interactions between the various market participants as well as to design market rules that maximizes efficiency and security. In this paper, the techniques of Agent-based Computational Economics (ACE) are employed to simulate the behavior of the GenCo participants in the National Electricity Market of Singapore (NEMS). The use of Reinforcement Learning (RL) in the agent-based modeling will be a more realistic representation of the GenCo and will bring about more accurate electricity market simulation outcomes. Actor-Critics constitute an important aspect of RL and in this paper we propose an adaptive actor-critic mapping using Particle Swarm Optimization (PSO). A simulation platform is built with the proposed model for Genco's learning and is tested in the conditions of varying vesting contract levels. Simulation results indicate that the proposed learning algorithm is able to procure higher GenCo revenue when benchmarked with existing learning algorithms.

This paper investigates some 6.6kV 5-level converter topologies, including 5-level neutral point clamped converter (NPC-5L), 5-level flying capacitor converter (FC-5L), 5-level active neutral point clamped converter (ANPC-5L) and 5-level hybrid converter, on the basis of state-of-the-art 4.5kV insulated gate bipolar transistors (IGBT). Semiconductor loss and capacitor loss distribution are compared under same equivalent switching frequency, with In-Phase Disposition (IPD) modulation method, which ensures a similar complexity of control implementation. In order to evaluate the performance and characteristics of these topologies, a 5-level as well as a 7-level cascaded H-bridge converter (CHB) with multiple isolated DC sources are also considered. Results show that except for hybrid type, all other 5-level converters have similar conduction loss, which is mainly determined by the number of switching devices in the current path. Switching loss in ANPC-5L is higher than NPC-5L and FC-5L due to its extra switching during zero crossing periods. On the other hand, capacitors loss in FC-5L is more significant than other topologies. By comparing the loss distribution with that in CHB, it can be known that there are still some margins in topology design for improvement to reduce the converter switching loss and capacitors loss, if high efficiency is the target.

Multilevel inverters are increasingly being used in high-power medium voltage applications due to their superior performance compared to two-level inverters. Among various modulation techniques for a multilevel inverter, the space vector pulsewidth modulation (SVPWM) is widely used. However, the implementation of the SVPWM for a multilevel inverter is complicated. The complexity is due to the difficulty in determining the location of the reference vector, the calculation of on-times, and the determination and selection of switching states. This paper proposes a general SVPWM algorithm for multilevel inverters based on standard two-level SVPWM. Since the proposed multilevel SVPWM method uses two-level modulation to calculate the on-times, the computation of on-times for an n-level inverter becomes easier. The proposed method uses a simple mapping to achieve the SVPWM for a multilevel inverter. A general n-level implementation is explained, and experimental results are given for three-level and five-level inverters

At high angular velocity, the induction motor is operated in the field weakening range due to the voltage limit of the inverter. Field oriented vector control (FOC) is unsuitable for this operation duetocoupling, non-linearities,andsaturationof linear current controllers. A proposed direct torque control space vector modulation (DTC–SVM) scheme using SVM does not use coordinate transforms or current controllers to achieve DTC. Control of the stator flux vector allows for dynamic change in the torque in all regions,including field weakening with the six-step operation. This paper describes the torque control dynamic in the field weakening range, using a step change in stator flux vector magnitude and it’s angular velocity. The method is demonstrated experimentally.

The asymmetrical half-bridge (AHB) topology discussed in this paper is one of the complementary driven pulse-width modulated converter topologies, which presents an inherent zero-voltage switching (ZVS) capability. In the previous work, the ideal operation of the converter and the ZVS realization process have been analyzed. However, the influence of the circuit parasitics on the output voltage drop and the design constraints of the circuit parameters to ensure the ZVS operation have not been investigated. The minimum load needed to ensure the ZVS operation is also not readily available. This paper presents a detailed and practical design for a 1-MHz AHB converter. A revised voltage transfer ratio of the converter is derived considering the influence of circuit parasitics and the ZVS transition. Two circuit parameters responsible for maintaining the ZVS operation are the transformer leakage inductance and the interlock delay time between the gate signals of two switches. A design method of the two parameters is proposed, which can ensure the ZVS transition. The possible ZVS range of the load variation is also investigated. A 50-W AHB converter with 1-MHz switching frequency was constructed, and a maximum efficiency of 91% was achieved.

Overmodulation achieves full utilization of installed dc link voltage, by modifying the magnitude and the phase of reference voltage vector. Due to the nonlinear behavior in this region, closed loop vector control becomes difficult. A simple method of overmodulation with closed loop control of stator flux vector is proposed in this paper. Instead of modifying the voltage reference by nonlinear equations or look up table, we propose a direct method to generate the switching times. In so doing, the average angular velocity of the stator flux vector is controlled linearly throughout the modulation range. The proposed method shows lower harmonic distortion compared with existing methods. It greatly reduces the complexity and cost of achieving overmodulation over existing schemes.

A constant switching frequency torque control method is presented in this paper, that uses flux error vector based space vector modulation (SVM) to achieve steady state and dynamic control of torque. The effect of SVM switching on torque ripple has been analyzed using "flux ripple vectors". This approach is used to develop an insight of torque ripple and to estimate it for any operating angular velocity. We propose a method of compensation to maintain steady state control of torque in the overmodulation region of operation. During torque dynamic, the optimum maximum switching state vector is selected. This gives a response similar to that obtained using direct torque control (DTC) and direct self control (DSC) methods. The proposed method is verified experimentally.

In the paper, a space-vector pulsewidth-modulation (SVPWM) inverter is proposed for constant-power operation of a two-phase induction motor. The operating principle of SVPWM is described, and the algorithm for constant-power operation is presented. Analysis for dynamic operation using a simple scalar control scheme is carried out and parameters for implementation of the scheme are obtained. Experimental investigation of the scheme is carried out and comparative analysis of the performance of the scheme is presented.