Mitigation possibilities to Greenhouse gas emissions from power production in India

Global warming is assertively the greatest environmental challenge for humans of 21st century. It is primarily caused by the anthropogenic greenhouse gas (GHG) that trap heat in the atmosphere. Because of which, the GHG emission mitigation, globally, is a critical issue in the political agenda of all high-profile nations. India, like other developing countries, is facing this threat of climate change while dealing with the challenge of sustaining its rapid economic growth. India’s economy is closely connected to its natural resource base and climate sensitive sectors like water, agriculture and forestry. Due to Climate change the quality and distribution of India’s natural resources may transform and lead to adverse effects on livelihood of its people. Therefore, India is expected to face a major threat due to the projected climate change. This study proposes possible solutions for GHG emission mitigation that are specific to the power sector of India. The methods discussed here will take Indian power sector from present coal dominant ideology to a system, centered with renewable energy sources. The study further proposes a future scenario for 2050, based on the present Indian government policies and global energy technologies advancements.

Electromagnetic and thermal design of a multilevel converter with high power density and reliability

Electric energy demand has been growing constantly as the global population increases. To avoid electric energy shortage, renewable energy sources and energy conservation are emphasized all over the world. The role of power electronics in energy saving and development of renewable energy systems is significant. Power electronics is applied in wind, solar, fuel cell, and micro turbine energy systems for the energy conversion and control. The use of power electronics introduces an energy saving potential in such applications as motors, lighting, home appliances, and consumer electronics. Despite the advantages of power converters, their penetration into the market requires that they have a set of characteristics such as high reliability and power density, cost effectiveness, and low weight, which are dictated by the emerging applications. In association with the increasing requirements, the design of the power converter is becoming more complicated, and thus, a multidisciplinary approach to the modelling of the converter is required. In this doctoral dissertation, methods and models are developed for the design of a multilevel power converter and the analysis of the related electromagnetic, thermal, and reliability issues. The focus is on the design of the main circuit. The electromagnetic model of the laminated busbar system and the IGBT modules is established with the aim of minimizing the stray inductance of the commutation loops that degrade the converter power capability. The circular busbar system is proposed to achieve equal current sharing among parallel-connected devices and implemented in the non-destructive test set-up. In addition to the electromagnetic model, a thermal model of the laminated busbar system is developed based on a lumped parameter thermal model. The temperature and temperature-dependent power losses of the busbars are estimated by the proposed algorithm. The Joule losses produced by non-sinusoidal currents flowing through the busbars in the converter are estimated taking into account the skin and proximity effects, which have a strong influence on the AC resistance of the busbars. The lifetime estimation algorithm was implemented to investigate the influence of the cooling solution on the reliability of the IGBT modules. As efficient cooling solutions have a low thermal inertia, they cause excessive temperature cycling of the IGBTs. Thus, a reliability analysis is required when selecting the cooling solutions for a particular application. The control of the cooling solution based on the use of a heat flux sensor is proposed to reduce the amplitude of the temperature cycles. The developed methods and models are verified experimentally by a laboratory prototype.

Impacts of increased levels of wind penetration on the Electric Power System of the Åland Islands

The application of VSC-HVDC technology throughout the world has turned out to be an efficient solution regarding a large share of wind power in different power systems. This technology enhances the overall reliability of the grid by utilization of the active and reactive power control schemes which allows to maintain frequency and voltage on busbars of the end-consumers at the required level stated by the network operator. This master’s thesis is focused on the existing and planned wind farms as well as electric power system of the Åland Islands. The goal is to analyze the wind conditions of the islands and appropriately predict a possible production of the existing and planned wind farms with a help of WAsP software program. Further, to investigate the influence of increased wind power it is necessary to develop a simulation model of the electric grid and VSC-HVDC system in PSCAD and examine grid response to different wind power production cases with respect to the grid code requirements and ensure the stability of the power system.

The role of nuclear and other conventional power plants in the flexible energy system

This thesis reviews the role of nuclear and conventional power plants in the future energy system. The review is done by utilizing freely accesible publications in addition to generating load duration and ramping curves for Nordic energy system. As the aim of the future energy system is to reduce GHG-emissions and avoid further global warming, the need for flexible power generation increases with the increased share of intermittent renewables. The goal of this thesis is to offer extensive understanding of possibilities and restrictions that nuclear power and conventional power plants have regarding flexible and sustainable generation. As a conclusion, nuclear power is the only technology that is able to provide large scale GHG-free power output variations with good ramping values. Most of the currently operating plants are able to take part in load following as the requirement to do so is already required to be included in the plant design. Load duration and ramping curves produced prove that nuclear power is able to cover most of the annual generation variation and ramping needs in the Nordic energy system. From the conventional power generation methods, only biomass combustion can be considered GHG-free because biomass is considered carbon neutral. CFB combusted biomass has good load follow capabilities in good ramping and turndown ratios. All the other conventional power generation technologies generate GHG-emissions and therefore the use of these technologies should be reduced.

Failure modes of passive decay heat removing safety systems of modern nuclear power plants

The purpose of this master’s thesis is to gain an understanding of passive safety systems’ role in modern nuclear reactors projects and to research the failure modes of passive decay heat removal safety systems which use phenomenon of natural circulation. Another purpose is to identify the main physical principles and phenomena which are used to establish passive safety tools in nuclear power plants. The work describes passive decay heat removal systems used in AES-2006 project and focuses on the behavior of SPOT PG system. The descriptions of the main large-scale research facilities of the passive safety systems of the AES-2006 power plant are also included. The work contains the calculations of the SPOT PG system, which was modeled with thermal-hydraulic system code TRACE. The dimensions of the calculation model are set according to the dimensions of the real SPOT PG system. In these calculations three parameters are investigated as a function of decay heat power: the pressure of the system, the natural circulation mass flow rate around the closed loop, and the level of liquid in the downcomer. The purpose of the calculations is to test the ability of the SPOT PG system to remove the decay heat from the primary side of the nuclear reactor in case of failure of one, two, or three loops out of four. The calculations show that three loops of the SPOT PG system have adequate capacity to provide the necessary level of safety. In conclusion, the work supports the view that passive systems could be widely spread in modern nuclear projects.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Modelling and simulation of a photovoltaic fuel cell hybrid system

A stand-alone power system is an autonomous system that supplies electricity to the user load without being connected to the electric grid. This kind of decentralized system is frequently located in remote and inaccessible areas. It is essential for about one third of the world population which are living in developed or isolated regions and have no access to an electricity utility grid. The most people live in remote and rural areas, with low population density, lacking even the basic infrastructure. The utility grid extension to these locations is not a cost effective option and sometimes technically not feasible. The purpose of this thesis is the modelling and simulation of a stand-alone hybrid power system, referred to as “hydrogen Photovoltaic-Fuel Cell (PVFC) hybrid system”. It couples a photovoltaic generator (PV), an alkaline water electrolyser, a storage gas tank, a proton exchange membrane fuel cell (PEMFC), and power conditioning units (PCU) to give different system topologies. The system is intended to be an environmentally friendly solution since it tries maximising the use of a renewable energy source. Electricity is produced by a PV generator to meet the requirements of a user load. Whenever there is enough solar radiation, the user load can be powered totally by the PV electricity. During periods of low solar radiation, auxiliary electricity is required. An alkaline high pressure water electrolyser is powered by the excess energy from the PV generator to produce hydrogen and oxygen at a pressure of maximum 30bar. Gases are stored without compression for short- (hourly or daily) and long- (seasonal) term. A proton exchange membrane (PEM) fuel cell is used to keep the system’s reliability at the same level as for the conventional system while decreasing the environmental impact of the whole system. The PEM fuel cell consumes gases which are produced by an electrolyser to meet the user load demand when the PV generator energy is deficient, so that it works as an auxiliary generator. Power conditioning units are appropriate for the conversion and dispatch the energy between the components of the system. No batteries are used in this system since they represent the weakest when used in PV systems due to their need for sophisticated control and their short lifetime. The model library, ISET Alternative Power Library (ISET-APL), is designed by the Institute of Solar Energy supply Technology (ISET) and used for the simulation of the hybrid system. The physical, analytical and/or empirical equations of each component are programmed and implemented separately in this library for the simulation software program Simplorer by C++ language. The model parameters are derived from manufacturer’s performance data sheets or measurements obtained from literature. The identification and validation of the major hydrogen PVFC hybrid system component models are evaluated according to the measured data of the components, from the manufacturer’s data sheet or from actual system operation. Then, the overall system is simulated, at intervals of one hour each, by using solar radiation as the primary energy input and hydrogen as energy storage for one year operation. A comparison between different topologies, such as DC or AC coupled systems, is carried out on the basis of energy point of view at two locations with different geographical latitudes, in Kassel/Germany (Europe) and in Cairo/Egypt (North Africa). The main conclusion in this work is that the simulation method of the system study under different conditions could successfully be used to give good visualization and comparison between those topologies for the overall performance of the system. The operational performance of the system is not only depending on component efficiency but also on system design and consumption behaviour. The worst case of this system is the low efficiency of the storage subsystem made of the electrolyser, the gas storage tank, and the fuel cell as it is around 25-34% at Cairo and 29-37% at Kassel. Therefore, the research for this system should be concentrated in the subsystem components development especially the fuel cell.

Review of the potential for co-firing of cassava rhizome for generating heat and power in cassava based bio-ethanol plant in Thailand

Biomass is an important source of energy in Thailand and is currently the main renewable energy source, accounting for 40% of the renewable energy used. The Department of Alternative Energy and E�ciency (DEDE), Ministry of Thailand, has been promoting the use of renewable energy in Thailand for the past decade. The new target for renewable energy usage in the country is set at 25% of the �nal energy demand in 2021. Thailand is the world’s fourth largest producer of cassava and this results in the production of signi�cant amounts of cassava rhizome which is a waste product. Cassava rhizome has the potential to be co-�red with coal for the production of heat and power. With suitable co-�ring ratios, little modi�cation will be required in the co-�ring technology. This review article is concerned with an investigation of the feasibility of co-�ring cassava rhizome in a combined heat and power system for a cassava based bio-ethanol plant in Thailand. Enhanced use of cassava rhizome for heat and power production could potentially contribute to a reduction of greenhouse gas emissions and costs, and would help the country to meet the 2021 renewable energy target.

System Integration of PV/T Collectors in Solar Cooling Systems

The demand for cooling and air-conditioning of building is increasingly ever growing. This increase is mostly due to population and economic growth in developing countries, and also desire for a higher quality of thermal comfort. Increase in the use of conventional cooling systems results in larger carbon footprint and more greenhouse gases considering their higher electricity consumption, and it occasionally creates peaks in electricity demand from power supply grid. Solar energy as a renewable energy source is an alternative to drive the cooling machines since the cooling load is generally high when solar radiation is high. This thesis examines the performance of PV/T solar collector manufactured by Solarus company in a solar cooling system for an office building in Dubai, New Delhi, Los Angeles and Cape Town. The study is carried out by analyzing climate data and the requirements for thermal comfort in office buildings. Cooling systems strongly depend on weather conditions and local climate. Cooling load of buildings depend on many parameters such as ambient temperature, indoor comfort temperature, solar gain to the building and internal gains including; number of occupant and electrical devices. The simulations were carried out by selecting a suitable thermally driven chiller and modeling it with PV/T solar collector in Polysun software. Fractional primary energy saving and solar fraction were introduced as key figures of the project to evaluate the performance of cooling system. Several parametric studies and simulations were determined according to PV/T aperture area and hot water storage tank volume. The fractional primary energy saving analysis revealed that thermally driven chillers, particularly adsorption chillers are not suitable to be utilizing in small size of solar cooling systems in hot and tropic climates such as Dubai and New Delhi. Adsorption chillers require more thermal energy to meet the cooling load in hot and dry climates. The adsorption chillers operate in their full capacity and in higher coefficient of performance when they run in a moderate climate since they can properly reject the exhaust heat. The simulation results also indicated that PV/T solar collector have higher efficiency in warmer climates, however it requires a larger size of PV/T collectors to supply the thermally driven chillers for providing cooling in hot climates. Therefore using an electrical chiller as backup gives much better results in terms of primary energy savings, since PV/T electrical production also can be used for backup electrical chiller in a net metering mechanism.

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Thermoeconomic analysis method for optimization of combined heat and power systems - part II

In this paper, a thermoeconomic functional analysis method based on the Second Law of Thermodynamics and applied to analyze four cogeneration systems is presented. The objective of the developed technique is to minimize the operating costs of the cogeneration plant, namely exergetic production cost (EPC), assuming fixed rates of electricity production and process steam in exergy base. In this study a comparison is made between the same four configurations of part I. The cogeneration system consisting of a gas turbine with a heat recovery steam generator, without supplementary firing, has the lowest EPC. (C) 2004 Published by Elsevier Ltd.

Load compensation in four-wire electrical power systems

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and another in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the methodology was adapted to accept different power factors for the system to be compensated. on the other hand, the determination of the compensation susceptances is based on the instantaneous values of the load currents. The results are obtained using the MatLab - Simulink environment.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. (C) 2011 Elsevier Ltd. All rights reserved.

Power system transmission network expansion planning using AC model

An optimisation technique to solve transmission network expansion planning problem, using the AC model, is presented. This is a very complex mixed integer nonlinear programming problem. A constructive heuristic algorithm aimed at obtaining an excellent quality solution for this problem is presented. An interior point method is employed to solve nonlinear programming problems during the solution steps of the algorithm. Results of the tests, carried out with three electrical energy systems, show the capabilities of the method and also the viability of using the AC model to solve the problem.