This study describes the development and use of a mathematical model that will enable mine operators to minimise the costs of electricity consumed by the ventilation and refrigeration systems used for environmental control in deep mines. This model was calibrated and tested by using actual data from a gold mine near Welkom in South Africa. In a first simulation, the mine's current practice of controlling conditions to a wet bulb temperature (Twb) of 25S°C, was optimised. The model demonstrated that this environmental condition could be sustained at lower electricity consumption. In so doing, the mine realised a saving of 30 000 kWh per day. The energy saving and load management led to a cost saving of R 1.5 million per year. However, a better indicator of environmental conditions is the Air Cooling Power index, (ACP). Research has shown that for hard physical work in hot conditions workers need an ACP of 300 w/m2. It was found that the case study mine actually supplied their workplace with a cooling capacity of 422 w/m2. The new model optimised the refrigeration and ventilation systems in such a manner that the workers were supplied with exactly 300 w/m2, no more and no less. It was found that by doing this, an electricity saving of 57 600 kWh per day could be realised when compared with the current mine practices. The energy saving and load management led to a potential cost saving of R 2.55 million per year. (Certain capital costs, such as for variable speed drives may have to be incurred to realise these savings.) The new model could be further extended to take advantage of the new Real Time Price offerings from Eskom It will be able to identify an operating point for the refrigeration and ventilation systems to supply 300 w/m2 for the workers, in real time, at the lowest electricity cost. / Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2004.

The aim of this research is to affirm the application of closed-loop optimal control for load shifting in plants with electricity tariffs that include time-of-use (TOU) and maximum demand (MD) charges. The water pumping scheme of the Rietvlei water purification plant in the Tshwane municipality (South Africa) is selected for the case study. The objective is to define and simulate a closed-loop load shifting (scheduling) strategy for the Rietvlei plant that yields the maximum potential cost saving under both TOU and MD charges. The control problem is firstly formulated as a discrete time linear open loop optimal control model. Thereafter, the open loop optimal control model is converted into a closedloop optimal control model using a model predictive control technique. Both the open and closed-loop optimal control models are then simulated and compared with the current (simulated) level based control model. The optimal control models are solved with integer programming optimization. The open loop optimal control model is also solved with linear programming optimization and the result is used as an optimal benchmark for comparisons. Various scenarios with different simulation timeouts, switching intervals, control horizons, model uncertainty and model disturbances are simulated and compared. The effect of MD charges is also evaluated by interchangeably excluding the TOU and MD charges. The results show a saving of 5.8% to 9% for the overall plant, depending on the simulated scenarios. The portion of this saving that is due to a reduction in MD varies between 69% and 92%. The results also shows that the closed-loop optimal control model matches the saving of the open loop optimal control model, and that the closed-loop optimal control model compensates for model uncertainty and model disturbances whilst the open loop optimal control model does not. AFRIKAANS : Die doel van hierdie navorsing is om die applikasie van geslote-lus optimale beheer vir las verskuiwing in aanlegte met elektrisiteit tariewe wat tyd-van-gebruik (TVG) en maksimum aanvraag (MA) kostes insluit te bevestig. Die water pomp skema van die Rietvlei water reiniging aanleg in die Tshwane munisipaliteit (Suid-Afrika) is gekies vir die gevalle studie. Die objektief is om 'n geslote-lus las verskuiwing (skedulering) strategie vir die Rietvlei aanleg te definieer en te simuleer wat die maksimum potensiaal vir koste besparing onder beide TVG en MA kostes lewer. Die beheer probleem is eerstens gevormuleer as 'n diskreet tyd lineêre ope-lus optimale beheer model. Daarna is die ope-lus optimale beheer model aangepas na ‘n geslote-lus optimale beheer model met behulp van 'n model voorspellende beheer tegniek. Beide die ope- en geslote-lus optimale beheer modelle is dan gesimuleer en vergelyk met die huidige (gesimuleerde) vlak gebaseerde beheer model. Die optimisering van optimale beheer modelle is opgelos met geheeltallige programmering. Die optimisering van die ope-lus optimale beheer model is ook opgelos met lineêre programmering en die resultaat is gebruik as 'n optimale doelwit vir vergelykings. Verskeie scenarios met verskillende simulasie stop tye, skakel intervalle, beheer horisonne, model onsekerheid en model versteurings is gesimuleer en vergelyk. Die effek van MA kostes is ook geevalueer deur inter uitruiling van die TVG en MA kostes. Die resultate toon 'n besparing van 5. 8% tot 9% vir die algehele aanleg, afhangend van die gesimuleerde scenarios. Die deel van die besparing wat veroorsaak is deur 'n vermindering in MA wissel tussen 69% en 92%. Die resultate toon ook dat die geslote-lus optimale beheer model se besparing dieselfde is as die besparing van die ope-lus optimale beheer model, en dat die geslote-lus optimale beheer model kompenseer vir model onsekerheid en model versteurings, terwyl die ope-lus optimale beheer model nie kompenseer nie. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

In South Africa, where 75% of the worlds platinum is produced, electricity tariffs have increased significantly over recent years. This introduces challenges to the energy intensive mineral processing industry. Within the mineral processing chain, run-of-mine ore milling circuits are the most energy-intensive unit processes. Opportunities to reduce the operating costs associated with power consumption through process control are explored in this work. In order to reduce operating costs, demand side management was implemented on a milling circuit using load shifting. Time-of-use tariffs were exploited by shifting power consumption of the milling circuit from more expensive to cheaper tariff periods in order to reduce overall costs associated with electricity consumption. Reduced throughput during high tariff periods was recovered during low tariff periods in order to maintain milling circuit throughput over a week long horizon. In order to implement and evaluate demand side management through process control, a load shifting controller was developed for the non-linear Hulbert model. Implementation of the load shifting controller was achieved through a multi-layered control approach. A regulatory linear MPC controller was developed to address technical control requirements such as milling circuit stability. A supervisory real-time optimizer was developed to meet economic control requirements such as reducing electricity costs while maintaining throughput. Scenarios, designed to evaluate the sensitivities of the load shifting controller, showed interesting results. Mill power set-point optimization was found to be proportionally related to the mineral price. Set-points were not sensitive to absolute electricity costs but rather to the relationships between peak, standard, and off-peak electricity costs. The load shifting controller was most effective at controlling the milling circuit where weekly throughput was between approximately 90% and 100% of the maximum throughput capacity. From an economic point of view, it is shown that for milling circuits that are not throughput constrained, load shifting can reduce operating costs associated with electricity consumption. Simulations performed indicate that realizable cost savings are between R16.51 and R20.78 per gram of unrefined platinum processed by the milling circuit. This amounts to a potential annual cost saving of up to R1.89 m for a milling circuit that processes 90 t/h at a head grade of 3 g/t. / Dissertation (MEng)--University of Pretoria, 2015. / Electrical, Electronic and Computer Engineering / Unrestricted

Wuxi Sino-Swedish Eco-City, a pilot city region with an area of 2.4 km2, is a demonstration project for innovation in energy technology and integrated smart city solutions in China. After the 1st phase of the project, general outlines of the city’s energy system were drawn and applicable technologies are provided. However, no work has been performed on building electricity load projection and load analysis. This thesis will therefore firstly focus on establishing the building electricity load projection model, using simulation software STELLA. Then the model is scaled up for the whole city region. The simulation results show that there is foreseen to be electricity peak in summer and winter, due to the cooling and heating demand. Based on simulation results, an electricity DSM (demand side management) strategy should be implemented in order to balance the load. Peak load shifting strategy is thus chosen to be investigated. Two technology options (ice-storage system and thermal storage system), which could be implemented to balance the electricity peak, is analyzed by scenarios. Also, commercial feasibility of implementing such technologies is discussed. / Wuxi Taihu Sino-Swedish Eco-City

The energy used by building sector accounts for approximately 40% of the total energy usage. In residential buildings, 30-60% of this energy is used for space heating which is mainly wasted by transmission heat losses. A share of 20-30% is lost by the discarded residential wastewater and the rest is devoted to ventilation heat loss.   The main objective of this work was to evaluate the thermal potential of residential wastewater for improving the performance of mechanical ventilation with heat recovery (MVHR) systems during the coldest periods of year. The recovered heat from wastewater was used to preheat the incoming cold outdoor air to the MVHR in order to avoid frost formation on the heat exchanger surface.   Dynamic simulations using TRNSYS were used to evaluate the performance of the suggested air preheating systems as well as the impact of air preheating on the entire system. Temperature control systems were suggested based on the identified frost thresholds in order to optimally use the limited thermal capacity of wastewater and maintain high temperature efficiency of MVHR. Two configurations of air preheating systems with temperature stratified and unstratified tanks were designed and compared. A life cycle cost analysis further investigated the cost effectiveness of the studied systems.   The results obtained by this research work indicated that residential wastewater had the sufficient thermal potential to reduce the defrosting need of MVHR systems (equipped with a plate heat exchanger) in central Swedish cities to 25%. For colder regions in northern Sweden, the defrosting time was decreased by 50%. The temperature control systems could assure MVHR temperature efficiencies of more than 80% for most of the heating season while frosting period was minimized. LCC analysis revealed that wastewater air preheating systems equipped with temperature stratified and unstratified storage tanks could pay off their costs in 17 and 8 years, respectively. / <p>QC 20190830</p>

Dans l'objectif de réduire les consommations énergétiques des bâtiments et de diminuer leur impact sur le réseau électrique, il est utile de disposer de stratégies de gestion énergétique en temps réel. Il s'agit en effet d'un verrou clé dans la perspective des réseaux intelligents (« smart grids ») et des programmes de gestion de la demande (« demand response »). Cette thèse propose ainsi le développement de stratégies de gestion en temps réel du chauffage électrique d'un bâtiment énergétiquement performant en période de pointe électrique. Tout d'abord, ces stratégies nécessitent l'utilisation et le développement de plusieurs modèles, à savoir un modèle de prévision météorologique, un modèle d'occupation et un modèle énergétique dynamique du bâtiment. Ensuite, dans l'objectif d'un suivi fiable des performances énergétiques et pour un pilotage optimal des installations, le calibrage du modèle de bâtiment à partir de relevés in situ est préférable. Une nouvelle méthodologie, basée sur un criblage des paramètres incertains et sur l'utilisation d'une méthode d'inférence bayésienne (calcul bayésien approché) a ainsi été développée. Enfin, deux méthodes d'optimisation ont été étudiées pour le développement de stratégies de régulation adaptées au temps réel. La première repose sur une méthode d'optimisation hors-ligne dont l'objectif est d'approximer les résultats d'une stratégie optimale calculée par une méthode d'optimisation exacte et ainsi identifier des lois de commandes simplifiées. La deuxième méthode repose quant à elle sur la commande prédictive et l'adaptation au temps réel de la commande optimale sous contraintes d'état et de commande utilisant la pénalisation intérieure. Une maison de la plateforme INCAS de l'Institut National de l'Énergie Solaire (INES) a été utilisée comme cas d'application pour étudier par simulation les différentes stratégies développées. / To reach the objectives of reducing the energy consumption of buildings and decreasing their impact on the electrical grid, it is necessary to elaborate real time control strategies in view of smart grids and demand response programs. In this context, this thesis aims at developing real time control strategies for electric load shifting in energy efficient buildings. First, these strategies require appropriate models regarding weather forecast, occupants' behaviour and building energy simulation. Then, in order to improve the reliability of building energy simulation and to ensure optimal control of facilities, a calibration process of the model based on on-site measurements is recommended. In this way a new methodology was developed , based on a screening technique and a bayesian inference method (approximate bayesian computation). Finally, two optimisation techniques were studied to develop real time control strategies. The first technique was based on offline optimisation methods. The principle is to approximate optimisation results (and more specifically model based predictive controllers results) and to extract simplified control strategies. The second method consisted in using model predictive control and, more precisely, in solving in real time a state and input constrained optimal control problem by interior penalty methods. An actual experimental passive house being part of the INCAS platform built by the National Solar Energy Institute (INES) was used to study by numerical simulation the different strategies developed.

Under the determined impulse of the European Union to limit the environmental impact of energy-related services, the electricity sector will face several challenges in coming years. Integrating renewable energy sources in the distribution networks is certainly one of the most urging issues to be tackled with. The current grid and production structure cannot absorb the high penetration shares anticipated for 2020 without putting at risk the entire system. The innovative concept of smart grid offers promising solutions and interesting implementation possibilities. The objective of the thesis is to specifically study the technical and economic benefits that the creation of an aggregator on the Swedish island of Gotland would imply. Comparing Gotland's power system characteristics to the broad variety of solutions offered by demand side management, wind power integration enhancement by demand response appeared particularly suited. A business case, specifically oriented towards the minimisation of transmission losses by adapting the electric heat load of private households to the local wind production was designed. Numerical simulations have been conducted, evaluating the technical and economic outcomes, along with the environmental benets, under the current conditions on Gotland. Sensitivity analyses were also performed to determine the key parameters for a successful implementation. A prospective scenario for 2020, with the addition of electric vehicles, has finally been simulated to estimate the long term profitability of an aggregator on the island. The simulation results indicate that despite patent technical benefits for the distribution network, the studied service would not be profitable in the current situation on Gotland. This, because the transmission losses through the HVDC-cable concern limited amounts of power that are purchased on a market characterized by relatively cheap prices and low volatility. Besides, the high fixed costs the aggregator has to face to install technical equipment in every household constitutes another barrier to its setting up.

Small and medium-scale companies are trying to minimise their carbon footprint and improve their cash flow, renewable installations are increasing all over the Europe and are expected to do so in following years. However, their dependency on the weather cause pressure on matching the production with demand. An option how to challenge this problem is by using energy storage. The aim of this project is to determine techno-economic benefits of Stirling engine and high temperature thermal energy storage for installation in energy user system and identify key factors that affect the operation of such system. In order to determine these factors simulations in Matlab were conducted. The Matlab linear programming tool Optisolve using dual-simplex algorithm was used. The sensitivity analysis was conducted to test the energy system behaviour. Economic evaluation was done calculating discounted savings. From the results, it can be seen the significant benefit of SE-HT-TES installation is the increased self-consumption of the electricity from PV installation. While the self-consumption in cases when there was no energy storage implemented was around 67 % and in one case as low as 50 % with the SE-HT-TES the value has increased up to 100 %. Energy cost savings are 4.7 % of the cost for the original data set and go up to 6.2 % when simulation with load shift was executed. Simulations have also shown that energy customer with predictable energy demand pattern can achieve higher savings with the very same system. It was also confirmed that for users whose private renewable production does not match load potential savings are 30 % higher compared to the system where energy load peak is matching the PV production peak. Simulations also shown that the customers located in areas with higher electricity price volatility can benefit from such system greatly.

The power grid faces major and escalating challenges in maintaining the power balance whilst society transitions towards increased sustainability. One promising solution to this challenge is found in the concept of demand response, where consumers adapt their energy demand due to some incentive in order to help balance the power grid. This study analyses the technical potential for industrial energy consumers to provide demand response by combining theory on demand response with theory on operations management and puts this to the test through a case study of a Swedish industrial sheet metal plant. In the study relevant factors such as energy and productivity parameters as well as planning and business models are shown to restrict the demand response potential. Different kinds of load shape objectives are analyzed, where peak clipping is shown to be simple but costly whilst load shifting is shown to be more complex but with the potential of offering demand response without affecting the overall productivity of the plant. These results help expand the picture of industrial consumer demand reponse from a static value depending on the economical incentive into a more complex concept that requires further research and optimization.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-11-07T10:48:05Z No. of bitstreams: 1 victorhugonetocortez.pdf: 3818044 bytes, checksum: a304eae89e97b941ff18b7233b80be9e (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-11-09T14:32:54Z (GMT) No. of bitstreams: 1 victorhugonetocortez.pdf: 3818044 bytes, checksum: a304eae89e97b941ff18b7233b80be9e (MD5) / Made available in DSpace on 2017-11-09T14:32:54Z (GMT). No. of bitstreams: 1 victorhugonetocortez.pdf: 3818044 bytes, checksum: a304eae89e97b941ff18b7233b80be9e (MD5) Previous issue date: 2017 / Grandes indústrias e parques industriais estão entre os maiores consumidores de energia de um país e sendo assim é interessante do ponto de vista do sistema elétrico fazer com que a energia utilizada por estes consumidores seja consumida de forma eficiente. Também é do interesse dos consumidores buscar a otimização do uso da energia, principalmente pela ótica da economia que pode ser alcançada nos custos e tarifas com energia elétrica. Uma das maneiras de se atingir este objetivo é através do Gerenciamento pelo Lado da Demanda onde se pode remanejar cargas de acordo com um objetivo preestabelecido, como por exemplo, a diminuição de custos com energia elétrica. Neste trabalho é apresentada uma metodologia para realocação de cargas em sistemas industriais. Esta metodologia utiliza cargas que estejam dentro de programas de controle direto de carga e busca encontrar um horário de alocação que traga a maior economia. A realocação é realizada através do uso de um algoritmo genético que tem como resultado final o horário otimizado de alocação de todas as cargas e consequentemente a curva de carga resultante. / Large industries and industrial parks are one of the largest energy consumers in a country and therefore it is essential to encourage that the energy consumed by those industries is done so efficiently. It is also in the best interest of the consumers to encourage those changes, especially regarding the economy that can be achieved by the embedded costs and tariffs. One of the ways to achieve this goal is to use a demand side management technique where loads can be shifted according to pre-determined objectives such as reducing the costs with electrical energy. In this thesis, a methodology for load shifting is presented for use in industrial systems. This methodology utilizes loads that are in direct load control programmes and aims to find the connection time that brings the most economical benefit. The reallocation is realized through the use of a genetic algorithm that has the best allocated time and load curve as a result.