<p>There are a huge number of customers using Ericsson’s prepaid telecom charging system. This means that even the smallest optimization done in its management of load results in a big win when considering time and CPU usage. Ericsson wishes therefore to learn about how the load management can be improved considering speed, i.e. faster response to load changes.</p><p>In this thesis work two subsystems of the prepaid telecom system are modeled. The first subsystem treats and prepares the telecom activity before it enters the second subsystem which checks if there is coverage for the action that the telecom activity asks for to be performed. A model which is an emulation of the subsystems is built using c++. It captures the timing of the real system not the logic or function. The c++ approach is compared to a Matlab approach which runs much slower in simulation. In addition, the model enables full view of the queue sizes and the rejects made by the regulator.</p><p>Verification has been performed with self generated data based on synthetic test cases. Verification and analysis show that the model captures the intended behavior of load management. Information about different software parameters and its influence on the output is obtained. Different scenarios are tested and the outcome discussed.</p>

Power measurement is important for various purposes such as revenue metering, power quality improvement, and direct load control. Various algorithms for power measurement have been proposed in the time domain, which implies a simple instrumentation, but more useful power formulations have been derived in the frequency-domain approach which would require appropriate sampling and measurement techniques to avoid long delay in processing voltage and current signals. The aim of this thesis to implement a measurement instrument that can measure the power components digitally and efficiently under sinusoidal and non-sinusoidal situations. We will use a high performance digital signal processing (DSP) chip and adopt a frequency domain based algorithm for the computation of power elements. The measurement system is expected to offer both high speed and accuracy, and can show wide spectra limited only by the sampling frequency.

Crop load management of fruit trees is a challenge for producers. For this experiment apple, peach, and sweet cherry trees were thinned using mechanical blossom thinning (MBT), and hand blossom thinning (HBT) to mimic MBT. Apple bloom was reduced by MBT, but only one treatment consistently reduced fruit set. Marketable yield, fruit weight, and quality were unaffected by thinning treatments. An apple spur leaf study found that damage from MBT was negligible. Mechanical blossom thinning of peach significantly reduced fruit set and hand thinning requirements at ‘June drop’. Marketable yield, fruit firmness, and soluble solids concentration were largely unaffected by thinning treatments, but fruit weight and size increased in one year. The two highest rates of sweet cherry MBT and HBT reduced fruit set but total yield, fruit weight, and quality were unaffected. Overall, MBT may be a viable option for tree fruit producers, especially peach growers. / The University of Guelph/OMAFRA Sustainable Production Systems Research Programme, the Niagara Peninsula Fruit and Vegetable Growers' Association, NSERC.

There are a huge number of customers using Ericsson’s prepaid telecom charging system. This means that even the smallest optimization done in its management of load results in a big win when considering time and CPU usage. Ericsson wishes therefore to learn about how the load management can be improved considering speed, i.e. faster response to load changes. In this thesis work two subsystems of the prepaid telecom system are modeled. The first subsystem treats and prepares the telecom activity before it enters the second subsystem which checks if there is coverage for the action that the telecom activity asks for to be performed. A model which is an emulation of the subsystems is built using c++. It captures the timing of the real system not the logic or function. The c++ approach is compared to a Matlab approach which runs much slower in simulation. In addition, the model enables full view of the queue sizes and the rejects made by the regulator. Verification has been performed with self generated data based on synthetic test cases. Verification and analysis show that the model captures the intended behavior of load management. Information about different software parameters and its influence on the output is obtained. Different scenarios are tested and the outcome discussed.

To achieve a high level of reliability, efficiency, and robustness in electric systems, the concept of smart grid has been proposed. It is an update of the traditional electric grid designed to meet current and future customers' requirements. With the smart grid, demand management has been adopted in order to shape the load pattern of the consumers, maintain supply-demand balance, and reduce the total energy cost. In this thesis, we focus mainly on energy savings by critically investigating the problem of load management in the smart grid. We first propose a user aware demand management approach that manages residential loads while taking into consideration users' comfort. This latter is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort or savings. The proposed approach is based on game theory using a modified regret matching procedure. It provides users with high incentives to participate actively in load management and borrows advantages of both centralized and decentralized schemes. Then, we investigate the issue of fairness within demand response programs. The fair division of the system bill stemming from the use of shared microgrid resources with different costs is examined. The Shapley value provides one of the core solutions to fairness problems; however, it has been known to be computationally expensive for systems such as microgrids. Therefore, we incorporate an approximation of the Shapley value into a demand response algorithm to propose a fair billing mechanism based on the contribution of each user towards attaining the aggregated system cost. Finally, we study energy trading in the smart grid as an alternative way to reduce the load on the grid by efficiently using renewable energy resources. We propose a solution that takes into account the smart grid physical infrastructure, in addition to the distribution of its users. Different constraints stemming from the nature of the smart grid have been considered towards a realistic solution. We show through simulation results that all of the proposed schemes reduce the load on the grid, the energy bills, and the total system energy cost while maintaining the users' comfort as well as fairness.

With the growing concerns about energy shortage and demand supply imbalance, demand side management (DSM) activities has found its way into the mining industry. This study analyzes the potential to save energy and energy-costs in underground mine ventilation networks, by application of DSM techniques. Energy saving is achieved by optimally adjusting the speed of the main fan to match the time-varying flow demand in the network, which is known as ventilation on demand (VOD). Further cost saving is achieved by shifting load to off-peak or standard times according to a time of use (TOU) tariff, i.e. finding the optimal mining schedule. The network is modelled using graph theory and Kirchhoff’s laws; which is used to form a non-linear, constrained, optimization problem. The objective of this problem is formulated to minimize the energy cost; and hence it is directly given as a function of the fan speed, which is the control variable. As such, the operating point is found for every change in the fan speed, by incorporating the fan laws and the system curve. The problem is solved using the fmincon solver in Matlab’s optimization toolbox. The model is analyzed for different scenarios, including varying the flow rate requirements and tariff structure. Although the results are preliminary and very case specific, the study suggests that significant energy and energy-cost saving can be achieved in a financially viable manner. / Dissertation (MEng)--University of Pretoria, 2014. / tm2015 / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Water Treatment Plants (WTPs) supply potable water which is transferred by pumps to various end users. WTPs and other sub-systems are energy intensive with pump installed capacities varying between 75 kW – 6 000 kW. It has therefore become important to optimise the utilisation of WTPs. Cost savings can be achieved and the load on the national grid can be reduced. The aim of this study is to develop and implement load management strategies on a municipal WTP. In this investigation the high lift pumps are deemed to be the largest consumers of electricity. Strategies to safely implement load management on a WTP were researched. By optimising the operations of the pumps, significant cost savings can be achieved. Comparisons between different electricity tariff structures were done. It was found plausible to save R 990 000 annually, on a pumping station with four 1 000 kW pumps installed, when switching to a time-of-use dependent tariff structure. Strategies to optimise plant utilisation while attempting a load management study include the optimisation of filter washing methods and raw water operations. An increase of 34% in efficiency for a filter backwash cycle was achieved. To accommodate the effects of the load management on the WTP, the operation of valves that allow water to distribute within the plant was also optimised. The implemented control strategies aimed to accomplish the full utilisation of the WTP and sub-systems to achieve savings. An average evening peak period load shift impact of 2.21 MW was achieved. Due to filter modifications the plant is able to supply 5% more water daily. A conclusion is drawn regarding the success of the strategies implemented. Recommendations are made for further research. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Water Treatment Plants (WTPs) supply potable water which is transferred by pumps to various end users. WTPs and other sub-systems are energy intensive with pump installed capacities varying between 75 kW – 6 000 kW. It has therefore become important to optimise the utilisation of WTPs. Cost savings can be achieved and the load on the national grid can be reduced. The aim of this study is to develop and implement load management strategies on a municipal WTP. In this investigation the high lift pumps are deemed to be the largest consumers of electricity. Strategies to safely implement load management on a WTP were researched. By optimising the operations of the pumps, significant cost savings can be achieved. Comparisons between different electricity tariff structures were done. It was found plausible to save R 990 000 annually, on a pumping station with four 1 000 kW pumps installed, when switching to a time-of-use dependent tariff structure. Strategies to optimise plant utilisation while attempting a load management study include the optimisation of filter washing methods and raw water operations. An increase of 34% in efficiency for a filter backwash cycle was achieved. To accommodate the effects of the load management on the WTP, the operation of valves that allow water to distribute within the plant was also optimised. The implemented control strategies aimed to accomplish the full utilisation of the WTP and sub-systems to achieve savings. An average evening peak period load shift impact of 2.21 MW was achieved. Due to filter modifications the plant is able to supply 5% more water daily. A conclusion is drawn regarding the success of the strategies implemented. Recommendations are made for further research. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.

All-electric ship power systems have limited generation capacity and finite rotating inertia compared with large power systems. Moreover, all-electric ship power systems include large portions of nonlinear loads and dynamic loads relative to the total power capacity, which may significantly reduce the stability margin. Pulse loads and other high-energy weapon loads in the system draw a large amount of power intermittently, which may cause significant frequency and voltage oscillations in the system. Thus, an effective real-time load management technique is needed to dynamically balance the load and generation to operate the system normally. Multi-agent systems, inspired by biological phenomena, aim to cooperatively achieve system objectives that are difficult to reach by a single agent or centralized controller. Since power systems include various electrical components with different dynamical systems, conventional homogeneous multi-agent system cooperative controllers have difficulties solving the real-time load management problem with heterogeneous agents. In this dissertation, a novel heterogeneous multi-agent system cooperative control methodology is presented based on artificial potential functions and reduced-order agent models to cooperatively achieve real-time load management for all-electric ship power systems. The technique integrates high-order system dynamics and various kinds of operational constraints into the multi-agent system, which improves the accuracy of the cooperative controller. The multi-agent system includes a MVAC multiagent system and a DC zone multi-agent, which are coordinated by an AC-DC communication agent. The developed multi-agent system framework and the notional all-electric ship power system model were simulated in PSCAD software. Case studies and performance analysis of the MVAC multi-agent system and the DC zone multi-agent system were performed. The simulation results indicated that propulsion loads and pulse loads can be successfully coordinated to reduce the impact of pulse loads on the power quality of all-electric ship power systems. Further, the switch status or power set-point of loads in DC zones can be optimally determined to dynamically balance the generation and load while satisfying the operational constraints of the system and considering load priorities. The method has great potential to be extended to other isolated power systems, such as microgrids.