The need to ease pressure from the depleting fossil fuel reserves coupled with the rising global energy demand has seen a drastic increase in research and uptake of renewable energy sources in recent decades. Of the commonly exploited renewable energy resources, hydropower is currently the most popular resource accounting for 17% of the world's total energy generation, a portion which translates to 85% of the renewable energy share. However, despite the huge potential, hydropower is dependent on the availability of water resource, which is affected by climate change. During wet seasons, hydropower system operators are faced with a deluge of floods which results in excess power generation and spillage. The situation reverses in dry seasons where system operators are compelled to curtail power generation because of low water levels in the hydro reservoirs. The later situation is more pronounced in drought prone regions such as Southern Africa where some hydropower plants are completely shut down in dry seasons due to water shortage. This dissertation focuses on the application of optimal control to hydropower plants with pumpback retrofits powered by on-site hydrokinetic and wind power systems. The first section of this work develops an optimal operation strategy for a high head hydropower plant retrofitted with hydrokinetic-powered cascaded pumpback system in dry season. The objective of pumpback operation is to recycle a part of the downstream discharged water back to the main dam to maintain a high water level required for optimal power generation. The problem is formulated as a discrete optimisation problem to simultaneously minimise the grid pumping energy demand, minimise the wear and tear associated with the switching frequency of the two pumps in cascade, maximise restoration of the reservoir volume through pumpback operation and maximise the use of on-site generated hydrokinetic power for pumping operation. Simulation results based on a practical case study show the pumping energy saving advantages of the cascaded pumping system as compared to a classical pumped storage (PS) system. The second section of this work develops an optimal control system for assessing the effects of ecological flow constraints to the operation of a hydropower plant with a hydrokinetic-wind powered pumpback retrofit. The aim of the control law in this case is to use the allocated water to optimally meet the contractual obligations of the power plant. The problem is formulated as a discrete optimisation problem to maximise the energy output of the reservoir subject to some defined technical and hydrological constraints. In this system, pumping power is met primarily by the wind power generator output supplemented by the on-site generated hydrokinetic power. The excess hydrokinetic power is exported to the grid to meet the committed demand. Three different optimisation scenarios are developed: The first scenario is the baseline operation of the hydropower plant without any intervention. The second scenario incorporates the hydrokinetic-wind-powered pumpback operation in the optimal control policy. The third scenario includes the downstream flow constraint to the optimal control policy of the second optimisation scenario. Simulation results based on a practical case study show that ecological flow constraints have negative effects to the economic performance of a hydropower plant. / Dissertation (MEng)--University of Pretoria, 2017. / MasterCard Foundation Scholars Program / Centre of New Energy Systems / University of Pretoria / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

A fast and efficient method to calculate time schedules for internal and boundary PRVs and flow modulation curves has been developed and implemented. Both time and flow modulation can be applied to a single inlet DMA. The time modulation methodology is based on solving a nonlinear programming problem (NLP). In addition, Genetic Algorithms (GA) has been proposed and investigated to calculate the optimal coefficients of a second order relationship between the flow and the outlet pressure for a PRV to minimize the background leakage. The obtained curve can be subsequently implemented using a flow modulation controller in a feedback control scheme. The Aquai-Mod® is a hydraulic device to control and modulate the outlet pressure of a PRV according to the valve flow. The controller was experimentally tested to assess its performance and functionality in different conditions and operating ranges. The mathematical model of the controller has been developed and solved, in both steady state and dynamic conditions. The results of the model have been compared with the experimental data and showed a good agreement in the magnitude and trends. A new method for combined energy and pressure management via integration and coordination of pump scheduling with pressure control aspects has been created. The method is based on formulating and solving an optimisation NLP problem and involves pressure dependent leakage. The cost function of the optimisation problem represents the total cost of water treatment and pumping energy. Developed network scheduling algorithm consists of two stages. The first stage involves solving a continuous problem, where operation of each pump is described by continuous variable. Subsequently, the second stage continuous pump schedules are discretised using heuristic algorithm. Another area of research has been developing optimal feedback rules using GA to control the operation of pump stations. Each pump station has a rule described by two water levels in a downstream reservoir and a value of pump speed for each tariff period. The lower and upper water switching levels of the downstream reservoir correspond to the pump being “ON” or “OFF”. The achieved similar energy cost per 1 Ml of pumped water. In the considered case study, the optimal feedback rules had advantage of small number of ON/OFF switches, which increase the pump stations lifetime and reduce the maintenance cost as well.

<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)