Generators bid into the deregulated electricity market in order to get committed & dispatched for meeting demands. In order to maximize their revenues & minimize the cost, systematic planning of the resources and analyzing the results is crucial to the success of any generation utility. UPLAN Network Power Model provides a convenient way to model & simulate the different expected conditions related to transmission, fuel costs & other variables which are of significant importance for generation planning and also allows us to analyze the way the output variables like capacity factors of generators, prices for Energy and Ancillary Services are affected by them. Based on a very simple model, this report describes the typical approach to UPLAN based resources planning & analyzes the significance of the results. Before that it also tried to understand the way UPLAN works for a very simple three bus model by stepwise introduction of complexity & analysis of results of the simulation runs. A few other issues like the Power Purchase Agreements, Congestion & Congestion Revenue Rights & the way Electricity is traded in the Deregulated Market are also presented. / text

Mining has a long history of production and operation management. Economies of scales have changed drastically and technology has transformed the mining industry significantly. One of the most important technological improvements is increased equipment, human, and plant tracking capabilities. This provided a continuous data stream to the decision makers, considering dynamic operational conditions. However, managerial approaches did not change in parallel. Even though many process improvement tools using equipment/human/plant tracking capabilities were developed (Fleet Management Systems, Plant Monitoring Systems, Workforce Management Systems etc.), to date there is no holistic approach or system to manage the entire value chain in mining. Mining operations are designed and managed around the already known system designated bottlenecks. However, contrary to common belief in mining, bottlenecks are not static. They can shift from one process or location to another. It is important for management to be aware of the new bottlenecks, since their decisions will be effected. Therefore, identification of true bottlenecks in real-time will help tactical level decisions (use of buffers, resource transfer), and identification of historical bottlenecks will help strategic-level decisions (investments, increasing capacity etc.). This thesis aims to address the managerial focus on the true bottlenecks. This is done by first identifying and ranking true bottlenecks in the system. The study proposes a methodology for creating Bottleneck Identification Model (BIM) that can identify true bottlenecks in a value chain in real-time or historically, depending on the available data. This approach consists of three phases to detect and rank the bottlenecks. In the first phase, the system is defined and variables are identified. In the second phase, the capacity, rates, and buffers are computed. In the third phase, considering particularities of the mine exceptions are added by taking mine characteristics into account, and bottlenecks are identified and ranked.

This thesis presents an investigation of two new techniques for eliminating redundancy inherent in uses of dynamic polymorphism operations such as virtual dispatches and type tests. The novelty of both approaches derives from taking a subject-oriented perspective which considers multiple applications to the same run-time values, as opposed to previous site-oriented reductions which treat each operation independently. The first optimization (redundant polymorphism elimination -- RPE) targets reuse over intraprocedural contexts, while the second (instance-specializing polymorphism elimination -- ISPE) considers repeated uses of the same fields over the lifetime of individual object and class instances. In both cases, the specific formulations of the techniques are guided by a study of intentionally polymorphic constructions as seen in applications of common object-oriented design patterns. The techniques are implemented in Jikes RVM for the dynamic polymorphism operations supported by the Java programming language, namely virtual and interface dispatching, type tests, and type casts. In studying the complexities of Jikes RVM's adaptive optimization system and run-time environment, an improved evaluation methodology is derived for characterizing the performance of adaptive just-in-time compilation strategies. This methodology is applied to demonstrate that the proposed optimization techniques yield several significant improvements when applied to the DaCapo benchmarks. Moreover, dramatic improvements are observed for two programs designed to highlight the costs of redundant polymorphism. In the case of the intraprocedural RPE technique, a speed up of 14% is obtained for a program designed to focus on the costs of polymorphism in applications of the Iterator pattern. For the instance-specific technique, an improvement of 29% is obtained for a program designed to focus on the costs inherent in constructions similar to the Decorator pattern. Further analyses also point to several ways in which the results of this work may be used to complement and extend existing optimization techniques, and provide clarification regarding the role of polymorphism in object-oriented design.

Today the modern power system is more dynamic and its operation is a subject to a number of constraints that are reflected in various management and planning tools used by system operators. In the case of hourly generation planning, Economic Dispatch (ED) allocates the outputs of all committed generating units, which are previously identified by the solution of the Unit Commitment (UC) problem. Thus, the accurate solutions of the ED and UC problems are essential in order to operate the power system in an economic and efficient manner. A number of computation techniques have progressively been proposed to solve these critical issues. One of them is a Particle Swarm Optimisation (PSO), which belongs to the evolutionary computation techniques, and it has attracted a great attention of the research community since it has been found to be extremely effective in solving a wide range of engineering problems. The attractive characteristics of PSO include: ease of implementation, fast convergence compared with the traditional evolutionary computation techniques and stable convergence characteristic. Although the PSO algorithms can converge very quickly towards the optimal solutions for many optimisation problems, it has been observed that in problems with a large number of suboptimal areas (i.e. multi-modal problems), PSO could get trapped in those local minima, including ED and UC problems. Aiming at enhancing the diversity of the traditional PSO algorithms, this thesis proposes a method of combining the PSO algorithms with a real-valued natural mutation (RVM) operator to enhance the global search capability and investigate the performance of the proposed algorithm compared with the standard PSO algorithms and other algorithms. Prior to applying to ED and UC problems, the proposed method is tested with some selected mathematical functions where the results show that it can avoid being trapped in local minima. The proposed methodology is then applied to ED and UC problems, and the obtained results show that it can provide solutions with good accuracy and stable convergence characteristic with simple implementation and satisfactory calculation time. Furthermore, the sensitivity analysis of PSO parameters has been studied so as to investigate the response of the proposed method to the parameter variations, especially in both ED and UC problems. The outcome of this research shows that the proposed method succeeds in dealing with the PSO' s drawbacks and also shows the superiority over the traditional PSO algorithms and other methods in terms of high quality solutions, stable convergence characteristic, and robustness.

Optimal transmission switching (OTS) enables us to remove selected transmission lines from service as a cost reduction method. A mixed integer programming (MIP) model has been proposed to solve the OTS problem based on the direct current optimal power flow (DCOPF) approximation. Previous studies indicated computational issues regarding the OTS problem and the need for a more accurate model. In order to resolve computational issues, especially in large real systems, the MIP model has been followed by some heuristics to find good, near optimal, solutions in a reasonable time. The line removal recommendations based on DCOPF approximations may result in poor choices to remove from service. We assess the quality of line removal recommendations that rely on DCOPF-based heuristics, by estimating actual cost reduction with the exact alternating current optimal power flow (ACOPF) model, using the IEEE 118-bus test system. We also define an ACOPF-based line-ranking procedure and compare the quality of its recommendations to those of a previously published DCOPF-based procedure. For the 118-bus system, the DCOPF-based line ranking produces poor quality results, especially when demand and congestion are very high, while the ACOPF-based heuristic produces very good quality recommendations for line removals, at the expense of much longer computation times. There is a need for approximations to the ACOPF that are accurate enough to produce good results for OTS heuristics, but fast enough for practical use for OTS decisions.

This thesis critically examines the nature and purpose of a series of four town-hall-like meetings, the Community Dialogues, held in the townships and suburbs of East London, South Africa, in 2009. They were undertaken by a mainstream, commercial newspaper, the Daily Dispatch, under the banner of the worldwide public journalism movement. Following Christians et al (2009), the thesis sets out a normative framework of media performance in a democracy, including a detailed and critical normative theory of the ‘facilitative role’ proposed and developed by Haas (2007), one of the public journalism movement’s key advocate-theorists. It also draws on a variety of theoretical frameworks and perspectives in the fields of Political Studies and Media Studies to provide an analytical overview of the complex matrix of political and media contexts – at the macro (global), meso (national) and micro (local) levels – that have helped give impetus to the Community Dialogues and also shaped their ongoing operation as a public journalism strategy in the South African context. Following a critical realist case study design, the thesis goes on to provide a narrative account of the Dialogues based on in-depth interviews exploring the motivations, self-understandings and perceptions of those journalists who originated, directed and participated in this project, as well as observation of a Community Dialogue, and an examination of some of the journalistic texts related to the Dialogues. This primary data is then critically evaluated against normative theories of press performance, especially Haas’s ‘public philosophy’ of public journalism. The thesis found that apart from their undoubted success in generating a more comprehensive and representative news agenda for the newspaper, the Dialogues often fell short of Habermas’s (1989) proceduralist-discursive notion of the ‘deliberating public’, which sees citizens share a commitment to engage in common deliberation and public problem solving. This can be attributed to a number of problems, including some important theoretical/conceptual weaknesses in the Community Dialogues’ project design, the relative immaturity of the project, the domination of civil society by political society in the South African political context, and a number of organisational constraints at the Daily Dispatch. On the other hand, the newspaper’s editorial leadership has shown clear commitment to the idea of expanding the project in the future, establishing a more a more structured programme of community engagement, and nurturing a more sustainable public sphere, including the building of a more dialectical relationship between the Dialogues and civil society.

Urban microgrids are smart and complex energy systems that help integrate renewables into our cities, turning our neighborhoods into partly energy self-sufficient hubs. Moreover, they create the space for electricity transactions between neighbors, transforming the former consumers into prosumers. The following work proposes the implementation of an urban microgrid to a neighborhood in Lisbon, Portugal. This dissertation’s objective is designing and discovering the optimal photovoltaic and storage capacity, optimal electricity dispatch, effects of distributed energy production in grid voltage and economic viability of such a system. With this purpose, a comprehensive model was elaborated, considering specific site weather data, electric loads, grid topology and utility tariffs. The self-sufficiency of Arco do Cego was found to be 66% in this study, reducing its carbon footprint by 61%. A detailed map of where to place each PV system and battery bank was generated, with specific electricity dispatch strategies. Moreover, the system was designed under real grid voltage, current and power flow constraints.

abstract: Traditional deterministic reserve requirements rely on ad-hoc, rule of thumb methods to determine adequate reserve in order to ensure a reliable unit commitment. Since congestion and uncertainties exist in the system, both the quantity and the location of reserves are essential to ensure system reliability and market efficiency. The modeling of operating reserves in the existing deterministic reserve requirements acquire the operating reserves on a zonal basis and do not fully capture the impact of congestion. The purpose of a reserve zone is to ensure that operating reserves are spread across the network. Operating reserves are shared inside each reserve zone, but intra-zonal congestion may block the deliverability of operating reserves within a zone. Thus, improving reserve policies such as reserve zones may improve the location and deliverability of reserve. As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With existing deterministic reserve requirements unable to ensure optimal reserve locations, the importance of reserve location and reserve deliverability will increase. While stochastic programming can be used to determine reserve by explicitly modelling uncertainties, there are still scalability as well as pricing issues. Therefore, new methods to improve existing deterministic reserve requirements are desired. One key barrier of improving existing deterministic reserve requirements is its potential market impacts. A metric, quality of service, is proposed in this thesis to evaluate the price signal and market impacts of proposed hourly reserve zones. Three main goals of this thesis are: 1) to develop a theoretical and mathematical model to better locate reserve while maintaining the deterministic unit commitment and economic dispatch structure, especially with the consideration of renewables, 2) to develop a market settlement scheme of proposed dynamic reserve policies such that the market efficiency is improved, 3) to evaluate the market impacts and price signal of the proposed dynamic reserve policies. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

The integration of variable renewable energy sources (VRE) is likely to cause fundamental and structural changes to the operation of future power systems. In the United Kingdom (UK), large amounts of price-insensitive and variable-output wind generation is expected to be deployed to contribute towards renewable energy and carbon dioxide (CO2) emission targets. Wind generation, with near-zero marginal costs, limited predictability, and a limited ability to provide upward dispatch, displaces price-setting thermal power plants, with higher marginal costs, changing flexibility and reserve requirements. New-build, commercial-scale, and low-carbon generation capacity, such as CO2 capture and storage (CCS) and nuclear, may impact power system flexibility and ramping capabilities. Low-carbon generation portfolios with price-sensitive thermal power plants and energy storage are therefore likely to be required to manage increased levels of variability and uncertainty at operational timescales. This work builds on a high-resolution wind reanalysis dataset of UK wind sites. The locations of existing and proposed wind farms are used to produce plausible and internally consistent wind deployment scenarios that represent the spatial distribution of future UK wind capacity. Temporally consistent electricity demand data is used to characterise and assess demand-wind variability and net demand ramp events. A unit commitment and economic dispatch (UCED) model is developed to evaluate the likely operating regimes of thermal power plants and CCS-equipped units across a range of future UK wind scenarios. Security constraints for reserve and power plant operating constraints, such as power output limits, ramp rates, minimum up/down times, and start-up times, ensure the operational feasibility of dispatch schedules. The load factors, time spent at different loads, and the ramping and start-up requirements of thermal power plants are assessed. CO2 duration curves are developed to assess the impacts of increasing wind capacity on the distribution of CO2 emissions. A sensitivity analysis investigates the impacts of part-load efficiency losses, ramp rates, minimum up/down times, and start-up/shut-down costs on power plant operating regimes and flexibility requirements. The interactions between a portfolio of energy storage units and flexible CO2 capture units are then explored. This multi-disciplinary research presents a temporally-explicit and detailed assessment of operational flexibility requirements at full 8760 hour resolution, highlighting the non-linear impacts of increasing wind capacity. The methodological framework presented here uses high spatial-and temporal-resolution wind data but is expected to provide useful insights for other VREbased power systems to mitigate the implications of inadequate flexibility.

Thesis submitted in fulfilment of the requirements for the degree Doctor of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2013 / The objective of the economic dispatch problem of electrical power generation is to schedule the committed generating unit outputs to meet the required load demand while satisfying the system equality and inequality constraints. The thesis formulates single area and multi-area Combined Economic Emission Dispatch (CEED) problem as single criterion, bi-criterion and multi-criteria optimisation problems based on fuel cost and emission criterion functions, constraints over the operational limits of the generator and the tie-lines, and requirements for a balance between the produced power and the system demand and power loss. Various methods, algorithms and softwares are developed to find solution of the formulated problems in single area and multi-area power systems. The developed methods are based on the classical Lagrange's and on the meta-heuristic Particle Swarm Optimisation (PSO) techniques for a single criterion function. Transformation of the bi-criteria or multi-criteria dispatch problem to a single criterion one is done by some existing and two proposed in the thesis penalty factors. The solution of the CEED problems is obtained through implementation of the developed software in a sequential way using a single computer, or in a data-parallel way in a Matlab Cluster of Computers (CC). The capabilities of the developed Lagrange's and PSO algorithms are compared on the basis of the obtained results. The conclusion is that the Lagrange's method and algorithm allows to receive better solution for less computation time. Data-parallel implementation of the developed software allows a lot of results to be obtained for the same problem using different values of some of the problem parameters. According to the literature papers, there are many algorithms available to solve the CEED problem for the single area power systems using sequential methods of optimisation, but they consume more computation time to solve this problem. The thesis aim is to develop a decomposition-coordinating algorithm for solution of the Multi Area Economic Emission Dispatch (MAEED) problem of power systems. The MAEED problem deals with the optimal power dispatch inside and between the multiple areas and addresses the environmental issue during the economic dispatch. To ensure the system security, tie-line transfer limits between different areas are incorporated as a set of constraints in the optimisation problem. A decomposition coordinating method based on the Lagrange's algorithm is developed to derive a set of optimal solutions to minimize the fuel cost and emissions of the multi-area power systems. An augmented function of Lagrange is applied and its decomposition in interconnected sub problems is done using a new coordinating-vector. Task-parallel computing in a Matlab Cluster is used to solve the multi-area dispatch problem. The calculations and tasks allocation to the Cluster workers are based on a shared memory architecture. Implementation of the calculation algorithm using a Cluster of Computers allows quick and simpler solutions to the multi-area CEED problem. The thesis applied the developed algorithms for the various problem formulation scenarios, i.e. fuel cost and emission function with and without valve point loading effect, quadratic and cubic fuel cost and emission functions. The various IEEE benchmark models are used to test the developed Lagrange's and PSO algorithms in the sequential, data-parallel, and task-parallel implementations. Developed methods, algorithms and software programmes can be applied for solution of various energy management problems in the regional and national control centres, smart grid applications, and in education and research institutions.