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Energy storage system (ESS) is generally used to manage the intermittency of the renewable energy sources (RESs). The proper control strategy is needed to effectively maintain the power balance between the RESs, load demand, and ESS. The conventional control strategy for the hybrid energy storage system (HESS) uses the high-/low-pass filter method for system net power decomposition and the ESS power dispatch. In this paper, a new joint control strategy is proposed for photovoltaic (PV) based DC grid system, with battery and supercapacitor (SC) as a HESS. The new joint control strategy utilizes the uncompensated power from the battery system to increase the performance of the overall HESS. The advantages of the proposed control strategy over the conventional control strategy are faster DC link voltage restoration and effective power sharing between the battery and the SC. The detailed stability analysis of the proposed control strategy is also presented. The effectiveness of the proposed control strategy over the conventional control strategy is validated with the short-term and long-term experimental studies. NRF (Natl Research Foundation, S’pore) Accepted version

The concept of community microgrid distribution system presents a new framework for operation and management of the future distribution systems with various operating constraints and a business model. It aims at extending the benefits of traditional microgrid by sharing the energy resources among multiple partners. Peer-to-peer (P2P) energy trading model seems to be one of the suitable models for energy trading in the future community microgrid distribution system. This paper proposes a hierarchical peer-to-peer (HP2P) energy trading framework for future community microgrid distribution networks to provide efficient energy trading in the microgrid energy market. The proposed HP2P framework is applied to a sample community microgrid distribution system. It results in cost saving and hence its effectiveness is verified by simulation study. Accepted version

A smart grid refers to an electricity transmission and distribution system that incorporates elements of traditional and cutting-edge power engineering, information technology, and communications, which can provide better grid performance and to support a wide array of additional custom services to consumers. A smart grid would facilitate the full use of sustainable energy technologies like solar power, wind power and fuel cells with the help of distributed energy storage systems (ESS). To understand the behavior of a smart grid, the author develops models suitable for overall analysis and design. The final goal is to lay the groundwork which would allow efficient management of the smart grid by solving all kinds of optimization problems, i.e., minimizing the operating costs, enhancing efficiency and reducing emission level while meeting the load demand. Smart Energy Management System (SEMS) is a core part for a smart grid system, which can make this system more intelligent. The optimal placement of the capacitors with the renewable energy is also discussed in this thesis. To handle the multi-objective optimization in the smart grid, a Jump and Shift method is proposed in this thesis. It aims to solve a large scale linear/nonlinear programming problem where the constraints are related to another large scale linear/nonlinear programming. A 14-bus and 112-bus power systems are tested to verify the multi-objective optimization algorithm based on the Jump and Shift method. ESS plays an important role in the smart grid. It is desirable to shave the peak demand and store the surplus electrical/renewable energy. A new method based on the cost-benefit analysis for the optimal sizing of an ESS in a microgrid (MG) is proposed. The Unit Commitment problem with the required spinning reserve capacity for the MG is considered in this method. To deal with the optimal control of ESS, the author presents an online management energy system for the lithium-ion (Li-ion) battery based on the proposed mathematical battery model and the adaptive Extended Kalman Filter (EKF) method. The proposed technique can be used to predict the state of charge (SOC) of the Li-ion battery via the online measured voltage and current. DOCTOR OF PHILOSOPHY (EEE)

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors such as driver behavior, location of charging stations, electricity pricing etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. Several studies have modelled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This thesis aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using the NetLogo software tool is employed in this thesis to closely mimic the human aggregate behaviour and its influence on the load demand due to charging of EVs. EV charging stations where the EV charging takes place will play an important role in the energy management of smart cities. Private and commercial EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. Hence, after the successful modelling of EV charging behavior using agent based approaches, a hybrid optimization algorithm for energy storage management is proposed as an application. This algorithm shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of PV integrated charging stations with on-site ESS. The algorithm comprises three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data and optimization for minimizing the operating cost of an EV charging station integrated with PV and ESS. An extensive simulation study is carried out with private and commercial EV charging load model obtained from the agent based modeling approach, in the context of Singapore, to check the effectiveness of this algorithm. Furthermore, a detailed analysis of the subsidy and incentive to be given by the government agencies for a higher penetration of PV systems is also presented. This work would aid in planning of adoption of PV integrated EV charging stations with on-site ESS which would be expected to take place of traditional gas stations in future. Doctor of Philosophy

This thesis presents the study of different types of control strategies for the hybrid energy storage system (HESS). The proposed control methods are applied in the standalone and grid-connected mode of operation of HESS. The control strategies studied are mainly focused on the effective power-sharing among the battery and supercapacitor (SC) based HESS, tight DC link voltage regulation with the improved dynamic response and simple controller design. Over the years, DC microgrids have been widely investigated due to their flexibility in integrating the renewable sources and energy storage devices. The photovoltaic (PV) and winds are the two most popular renewable energy sources (RESs). The main issue with these RESs is their intermittent nature. The power generation from these RESs is easily affected by the varying environmental conditions. Thus, the microgrid requires energy storage systems (ESSs) which ensure a continuous power to the load demand. ESSs are the major part of the microgrids. ESSs complement the supply and demand mismatch and ensure the stability of the microgrid. In recent days, ESSs with the different operating characteristics are combined to form a HESS in the microgrid applications. In the HESS, the characteristics of the various ESSs can be effectively and efficiently utilized. The effective utilization of the HESS in the microgrid depends on the control techniques implemented to maintain the power balance through the HESS. Hence, the proper control method is of utmost importance for maintaining the power balance in the system with the load and generation variation. The faster joint control strategy is presented for the effective control of the battery and SC based HESS in the DC microgrid applications. The HESS handles the excess or deficit power to maintain the power balance in the system. In general, the battery supports the slow varying power requirement and the SC supports the high-frequency power demand. To extract the slow varying power demand and the transient power demand from the total power demand low-pass filter (LPF) is used. In conventional control methods, the power decomposition entirely depends on the performance of LPF. It also neglects the uncompensated power from the battery system. In the proposed control method, the uncompensated power from the battery system is handled by the SC system. This improves the overall system response to the PV generation fluctuations and load disturbances providing a faster DC link voltage restoration. The model predictive control (MPC) method has been intensively studied for the industrial process control for more than four decades. The MPC method has been extensively applied in many power electronics topologies in last few decades. The conventional MPC method uses a discrete system model and the complex optimization algorithms to solve the control objectives in the defined time period. This approach requires large computational resources to obtain optimal control parameters. This hinders its utilization for the system which requires a faster control action with less computational burden. The enumeration-based model predictive control (EMPC) methods are generally used to solve these issues. The EMPC methods require less computational resources and can be easily applied in the digital signal processor (DSP) control boards. The enumeration based model predictive current control (EMPCC) method for the battery and SC based HESS in the DC microgrid applications is proposed. The proposed control method is divided into two parts: (i) DC link voltage control loop and (ii) Inner current control loop. The PI compensator is used to design the DC link voltage control loop. The PI controller generates the total current references for the HESS. The inner current control loop is based on EMPCC method. The inner current control regulates the total current references for the HESS. The advantages of the proposed method are less computational complexity, simple controller design, direct manipulation of control signals without the pulse width modulation (PWM) modulator and better dynamic response. The study of the dynamic evolution control (DEC) method for the HESS control is also presented in this thesis. The DEC method uses the pre-defined trajectory known as evolution path to minimize the error state in the system. The difference between the reference value and the measured system output is known as error state. A new power sharing approach for the HESS is also introduced to calculate the current references for the battery and SC. The current is regulated using the DEC method. The control equation of the DEC method consists of a predictive term and a feed-forward term which handle the current from HESS to stabilize the DC link voltage. The performance of the DEC is compared with the conventional PI-based control methods with the simulation and experimental case studies. The results obtained exhibit a better dynamic performance compared to that of the conventional control techniques. The DEC method is simple to implement as it requires less computational resources. The grid-connected mode of operation of the HESS is another aspect of the research work presented in this thesis. A new energy management system (EMS) is proposed which help in effective and efficient utilization of the HESS in the grid-connected mode of operation. The main advantages of the proposed EMS and control method are better DC link voltage restoration to PV generation and load disturbances, efficient power balance between the battery and SC, dynamic power-sharing based on the battery state of charge (SOC), decreased rate of charge/discharge of the battery current, enhanced power quality in AC grid and smooth mode transitions. The scaled down experimental platform is built to verify the proposed control methods. The experimental case studies for the proposed control methods are carefully designed and implemented using the dSPACE 1103 real-time controller platform. The obtained simulation and experimental results are compared with that of the existing control methods. The results validate the effectiveness of the proposed control methods. The results are compared in terms of faster dynamic response and the complexity of the control method. The short-term and long-term experimental case studies are conducted. They show the proposed control methods are effective than the conventional control methods. Doctor of Philosophy

While designing electrical power systems, underground cable ampacity is extremely important. There are several methods to calculate ampacity. For similar installation, different methods can give diverse ampacity values. The assumptions and relevant parameters must be calculated correctly to perform accurate sizing of power cables for the respective ampacity calculation method. In this master dissertation, the aim is to study, analyze and provide a standardized analytical ampacity calculation for a specific power cable. The conditions under which the ampacity calculation is performed are detailed. Analytical calculations are performed using two methods based on two international standards namely IEEE and IEC. This dissertation details the assumptions used in each method and the reasons of differences in the ampacity values calculated employing these two methods. Software calculations are performed using two software namely ETAP and MATLAB/Simulink. For the specific cable, Important parameters such as resistance, reactance, inductance and impedance are calculated using the ETAP software and these calculated values are substituted in a MATLAB/Simulink model. Master of Science (Power Engineering)

Nowadays with the advent and increased usage of power electronics devices the effect of the power quality (PQ) disturbances for power utilities and customers is becoming a matter of great concern. These disturbances should be detected rapidly and accurately in order to protect the power system and the connected equipment. In this project, hybrid power system and experimental microgrid testbed present in CERL, EEE, NTU, Singapore is taken for the study of detection of various power quality disturbances using proposed DTCWT method. Various renewable energy sources such as wind energy system and solar photovoltaic are incorporated with distributed generators such as diesel engine generators to reduce the intermittency. Various energy storage devices such as flywheel and batteries are also integrated in the hybrid power system network to meet the load demand and improving the power management. The power quality disturbances are simulated and detected using system modelling with the help MATLAB/Simulink software tool as well as in real time digital simulator OPAL-RT by applying signal processing techniques such as standard wavelet transform and DTCWT. A comparison study is conducted for disturbance detection performance in case of proposed DTCWT technique as compared to conventional WT under various operating condition. Also, various performance parameters like SD and energy content are computed to make a distinction between system operating under the influence of power line disturbances and normal system operation based on a selected threshold value. Significant improvement in performance of DTCWT over the conventional wavelet transform is observed from the case study presented in the form of comparative analysis for detecting various power line disturbances. The results are validated in real time operating condition using OPAL-RT digital simulator. Master of Science (Power Engineering)

Many investigations and research works have been conducted on the National Electricity Market of Singapore (NEMS) by market regulators and researchers. It is well-known for its distinguished maturity and structure. A demand response (DR) program was implemented in April 2016 to further improve its efficiency and competitiveness. With the liberalization and deregulation of the electricity market, it becomes more flexible for demand side to actively participate in the wholesale electricity market. This has changed the way how electricity is traded. In this research, a software-based intelligent decision support system is developed for participation of loads in the demand response program of the wholesale electricity market of NEMS. This work focuses on the study of the Singapore demand response program featuring demand side bidding and incentive payments. The impact of demand side participation on the market is analyzed. The issues associated with the structure and operation of the electricity market are addressed in this research. In addition, DR can be employed in energy management to mitigate the difficulty brought by uncertain demand. Firstly, the current market clearing model (MCM) of Singapore, which is an assessment tool for the simultaneous allocation of energy and ancillary services, is introduced. The principle of transmission loss and nodal prices integrated in the MCM is presented as well. Secondly, the methodology of integration of demand side bidding with the newly introduced requirements is discussed. Finally, a stochastic optimization is proposed for the coordinated operation of generating units and demand response to adequately deal with the uncertain electricity demand. The coordination is accomplished through a twostage stochastic programming model. In the future, the proposed modeling can be utilized for further evaluation on real time demand response (DR) and interruptible load (IL) strategies. The challenges to be addressed are also outlined while the solution is still under testing. Master of Engineering

In recent years, energy supply from renewable energy sources (RESs) is a main trend towards future smart grid. Many countries including Singapore have increased their energy supply from RESs. However, power fluctuation can become more severe and unpredictable with the increase of RESs, which brings risk to the stability and reliability of power systems. To address the problem, more battery energy storage systems (BESSs) are connected to modern power systems. The optimal power coordination among different energy resources considering lifespan of BESSs is crucial. The author first establishes a state space model for the aging effect of lithium-ion battery. The effect of battery is later used as constraints for control. Then, the objectives are divided into two categories, long-time-scale and short-time-scale applications. The roles of BESSs in these applications are respectively investigated. For long-time-scale applications, peak load shifting is studied because it can save energy and reduce energy cost. For short-time-scale applications, the author focuses on real-time power balancing and power smoothing. There are two kinds of approaches, conventional PI (proportional–integral) controller and MPC (Model Predictive Control). They are studied and simulated separately. Finally, the comparison between them demonstrates the advantages of the proposed MPC approach. The key idea of this dissertation is to maintain system stability according to the swing equation. Meanwhile we keep batteries in the optimal status. The significance of this study can be summarized as the pursuit of secure and economical operation of a power system. Key words: BESS, battery aging effect, MPC, load frequency control Master of Science (Power Engineering)

Solar intermittency is a critical issue which limits the development of grid-connected photovoltaic system in the applications of solar energy. High accuracy of solar irradiance forecasting plays an important role in establishing modern photovoltaic generation power system. With the prediction data provided, the system will take appropriate actions to control the energy storage system. This thesis focuses on one of the methods of short-term solar irradiance forecasting based on ground-based sky imager, i.e. sky images captured by Total Sky Imager (TSI) and Wide Angle High- Resolution Whole Sky Imaging System (WAHRSIS). The images obtained will be processed to determine the cloud cover and analyzed to estimate the cloud motion velocity by using the MPIV algorithm. The cloud condition of next few minutes will be predicted and consequently the solar irradiance values can be calculated based on the program developed in this project. The sample results taken in this project show that the program in this project can forecast the solar irradiance in specific location for about 5-15 minutes with mean prediction error about 20%. The intra-hour solar irradiance can be obtained by consecutively collecting images into the program. Depending on different locations and sky imager systems, the same algorithm can be applied accordingly by calibrating the threshold value for cloud determination and image resolution to forecast the solar irradiance. Master of Science (Power Engineering)

With the increasing installed capacity of renewable energy sources (RESs) in the power systems, their uncertainty and intermittency are causing power imbalances in the grid, which leads to frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation resources to recover the frequency within a short period of time. While traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signals, battery energy storage systems (BESSs) are considered as an effective regulation source to respond immediately to frequency deviations. From the system operator's point of view, a proper procedure for BESS sizing and control is required to ensure a long-term high quality regulation service on the system level. We study the sizing issue of a BESS aggregation based on the overall system control performance. The frequency performance with different penetration rates of BESS is analyzed. The frequency performance is evaluated by Control Performance Standard 1 (CPS1) and Control Performance Standard 2 (CPS2) proposed by North American Electricity Reliability Corporation (NERC). The recommended BESS penetration rate in the power system is analyzed using an iterative approach according to the NERC performance indices. Taking the investment cost into consideration, a 5% to 15% BESS penetration rate is recommended for the test system. For a network penetrated with dispersedly located RESs, distributed energy resources (DERs) and storage devices, the virtual power plant (VPP) concept is applied to aggregate the resources. A centralized hierarchical controller is proposed for the VPP to achieve a more economical operation and more effective system frequency regulation. Model predictive control (MPC) strategy is implemented in the controller. The VPP's influence on system frequency is studied by real-time simulations. The results show that the secondary control executes economic dispatch to coordinate the power dispatch within the VPP according to photovoltaic (PV) power output, load data, and real-time electricity tariff. In the meantime, the primary controller is capable of stabilizing system frequency within the permitted range during heavy load and peak solar generation periods. The proposed hierarchical control scheme is further developed for a distribution network with distributed BESSs which can be aggregated as a VPP. As it is difficult for BESSs to be profitable due to high battery costs, the aggregated BESSs are maneuvered to participate in multiple markets. We propose a hierarchical energy management system (HiEMS) for aggregated BESSs taking part in both energy and regulation markets. The HiEMS performs optimal scheduling in multiple markets and attempts to coordinate BESSs of different battery types, various state of charge (SOC), and power and energy capacity. The SOC values are regulated around the expected average SOC to prevent individual saturation or depletion, and thus increasing the average battery lifetime. The proposed HiEMS can support up to 0.5 regulation participation rate, which will boost the cost performance index (CPI) by 7 times. HiEMS outperforms participation factor (PF) method by 1.24 times and master-slave (M-S) method by 1.4. In terms of time of first replacement, HiEMS is 2.15 times loner than that of the PF method and 3.75 times longer than that of the MS method. In current markets, the performance-based regulation market in Pennsylvania-Jersey-Maryland power pool, i.e. PJM Interconnection Company, is the most profitable one for BESSs. While the BESSs are always assumed to deliver very high performance, an optimal schedule optimizer is proposed including an innovative realistic BESS performance model. The performance model, which characterizes the trade-off between the performance and the power bids in both energy and regulation markets, contributes to the regulation energy scheduling and guarantees BESS performance. The schedule optimizer also models the pricing uncertainties in both markets by generating scenarios according to their respective statistical characteristics. After adopting the proposed optimal scheduling strategy, the cost-performance index (CPI) of the BESSs governed by HiEMS is boosted to 31.70%, compared to around 10% from only energy market participation. Doctor of Philosophy (EEE)

State Estimation (SE), first proposed by F. C. Schweppe in 1970s, is an essential module in Energy Management System (EMS). The phasor voltage of each bus in the system is estimated based on the measurements, such as power flows, power injection, voltage magnitudes and etc. The estimated results are later utilized to identify bad measurements/inconsistent topology and evaluate the system original condition. It can be claimed that power system State Estimator constitutes the core module for the on-line security analysis. Smart power grids refer to the modern power systems with the advanced technologies in communication, system control and operation. In contrast to the traditional electric power systems, the smart power grids are advantageous by equipping with computer-based remote control and automation, intelligent devices supported by two-way communication technologies, active consumers and renewable energy resources. High efficiency of energy utilization can be expected in smart power grids. Traditional monitoring and control techniques over power systems will not be suitable for the smart power grids, both in the transmission and the distribution levels. In this thesis, conventional SE methods are improved to cater for the needs of smart power grids. Phasor Measurement Units (PMUs) are firstly introduced and their application in SE is reviewed. Prior to utilizing PMU measurements or synchrophasor measurements in SE, the placement strategy on PMUs is studied over various test systems. Case study results verify that the measurement redundancy can be improved via the proposed techniques. Next, SE in smart transmission systems is conducted. In this work, the adaptive underfrequency load shedding (UFLS) procedure is considered as the pseudo measurements to improve the SE results. By correcting the power injection measurements at the unobservable buses where load shedding takes place, it enables the State Estimator to track the system states more accurately. Case studies on the IEEE test systems are conducted to verify the results. For SE in smart distribution systems, the study has been carried out on the newly emerging hybrid AC/DC microgrids. A decentralized technique is proposed for the estimation by modelling the AC and DC microgrids separately and utilizing the power flow measurements obtained from the connecting line of the two networks as an equality constraint. By virtue of PMUs, SE in the AC network is formulated in a three-phase linear manner and the unbalanced problem can be investigated. SE in the DC network is built in a linear manner as well, based on the voltage-current relationship. The decentralized SE can perform comparatively with the centralized technique regarding the accuracy, computational speed and bad data detection, while the shared measurement information is reduced and data privacy can thus be improved. DOCTOR OF PHILOSOPHY (EEE)

Fluctuating and intermittent irradiance interrupts the photovoltaic (PV) power generation. Massive PV grid-connection may bring about the stability problem to running of the power system. The forecast of output power of a PV plant can balance the dispatch of conventional energy (thermal power, hydropower, etc.) and photovoltaic power, and ensure the power supply quality. Irradiance, as the main factor affects the output power of PV power generation. This will lead to the fluctuation of the output power- due to its volatility and mutability, which brings huge difficulty to the PV power prediction. The traditional forecasting method, which is largely based on physical and statistical models, will bring about the lag to the prediction results. Thus it is unable to provide accurate prediction results continuously. Relevant research results have indicated that the primary cause of the sudden change of irradiance is the clouds’ coverage against the sunlight. The prediction of cloud coverage above the solar panel can be achieved as follows: proceed with all-day automatic observation and image acquisition by using ground-based remote sensing cloud detection equipment, then using image processing technique to extract the feature of the clouds’ future movement. The Total Sky Imager™ is designed to capture the cloud coverage data. And a mechanism aiming at parameterization the cloud image serves the purpose. Since the cloud images are captured far from the ground-based camera, the resolution is low and the noisy points are inevitable. This requires elaborate image processing techniques to eliminate the interference factors, making the quantized irradiation and coverage data more valuable. Master of Science (Power Engineering)

This project introduces the characteristics of photovoltaic (PV) panels and storage battery system in the Singapore distribution power system. Their impacts on the power grid are studied as well. The penetration of the PV and storage battery system in a small Singapore HDB distribution power network will be discussed. The effects on the power flow and voltage will be studied based on the comparison between the simulation results obtained before and after the penetration of the PV and storage battery system. The frequency regulation will be examined by performing the simulation of the PV and storage system in a two-area model using the concept of Virtual Power Plant (VPP). After that, the control mechanism of the storage battery will be designed with a local LFC algorithm. The actual frequency data of the Singapore power grid will be measured so as to test the control algorithm. The dynamic simulation of the mechanism will also be examined. Master of Science (Power Engineering)

With the launching of Intelligent Energy System (IES) pilot project in Singapore, 4,500 smart meters will be installed in various residential, commercial and industrial locations, including the Nanyang Technological University (NTU) campus, the CleanTech Park at Jalan Bahar and the Punggol Eco-Precinct. The purpose of the IES pilot project is to test and evaluate new applications and intelligent technologies around a smart grid to enhance its efficiency and robust resilience. It will serve as a test bed for Singapore power grid to become smarter and cleaner in the future. Master of Science (Power Engineering)

98 p. In the operation of a microgrid, forecasting of load is an important task. A microgrid, is an integrated energy system comprises price-sensitive loads, energy storage and Distributed Energy Resources (DERs) which could supply power and heat. To operate such an integrated system, Microgrid Energy Management System (MG-EMS) is required to monitor and control these devices. Load forecasting which predicts the total customer load in a district plays an important role in the MG-EMS. Some MG-EMS's functions such as economic dispatch require load forecast with shorter forecast lead time, from a single minute to several dozen minutes and this is referred to as very short-term load forecasting (VSTLF). VSTLF has a significant impact on the efficient market operation, controlling and planning of microgrids. The accuracy of the forecast greatly depends on the algorithms used in prediction. Load forecasting algorithms which give accurate forecast values will result in economic savings as well as enhancing security of the system operation. With the 24-hour ahead load forecast values, Unit Commitment which determines the half-hourly (or quarter-hourly) on/off schedules of DERs and their power generation for the next 24 hours can be executed. Master of Science (Power Engineering)

Parallel implementation of power system computation has gained much attention due to recent advances in parallel computation technology and the development of parallel processors. With the rapid progress of computer science, power system computation has become more and more sophisticated and there are always some new performance requirements to the computers, software and algorithms. Master of Engineering