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

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.

With the increasing demand for renewable energy sources, new systems are being developed to sustain future infrastructure, accommodating these new energy sources. One of the proposed solutions is to incorporate distributed energy resources to different households in order to provide local energy demands effectively. To enable large-scale integration of flexible energy resources, it is crucial to reduce end-user energy and power costs, which can be done by designing an optimization model objected to minimize the total electricity bill. In the scope of this Master thesis, the interest lies in investigating a control strategy to operate batteries, heat pumps, and other assets by producing the optimal setpoints using the designed optimization algorithm that takes, amongst others, market and weather data as well as customer behavior into account. The applied method for producing these setpoints is sensitivity analysis in linear programming, and heat pump scheduling has been investigated for performance evaluation of this technique. The results show that applying this method produces the optimal setpoints over the non-controllable electricity load range by utilizing a low number of optimizations, i.e. high computation-efficiency, and high accuracy. Consequently, the controller by having the given setpoints as the input can easily adjust the heat pump output power based on the real-time non-controllable electricity load without creating any peaks and extra costs for the customers. / Med en ökad efterfrågan på förnybara energikällor utvecklas nya system för att upprätthålla framtida infrastruktur vilket kommer säkra dessa nya energikällor. En av de föreslagna lösningarna är att integrera distribuerade energiresurser till olika hushåll för att effektivt kunna tillgodose lokala energikrav. För att möjliggöra en storskalig integrering av flexibla energiresurser det avgörande är att man kan minska slutkundens energi och effektkostnader. Detta kan nås genom att utforma en optimeringsmodell av problemet som tar hänsyn till olika resourses begränsningar osv. för att minska elkosnaden hos slutkunden. Syftet med detta examensarbete är att undersöka en kontrollstrategi för att använda batterier, värmepumpar och andra tillgångar på ett optimalt sätt, genom att producera de optimala börvärdena med hjälp av den utformade optimeringsalgoritmen som tar hänsyn till bland annat marknads och väderdata samt kund beteende. För att producera dessa börvärden användes methoden känslighetsanalys som är en del inom linjär programmering och fokus har varit styrningen av värmepumpar. Resultaten visar att tillämpningen av denna metod leder till att de optimala börvärdena över det icke-kontrollerbara elektriska lasten erhålles, med ett lågt antal optimeringar, dvs att metoden har hög beräknings-effektivitet samt noggrannhet. Följaktligen kan regulatorn med de givna börvärdena som ingång enkelt justera värmepumpens utgångseffekt baserat på realtids icke-kontrollerbar elektriska lasten, utan att skapa några toppar och extra kostnader för kunderna.

<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)

Water distribution networks are a fundamental part of any modern city and their daily operations constitute a significant expenditure in terms of energy and maintenance costs. Careful scheduling of pump operations may lead to significant energy savings and prevent wear and tear. By means of computer simulation, an optimal schedule of pumps can be found by an optimisation algorithm. The subject of this thesis is the study of pump scheduling as an optimisation problem. New representations of pump schedules are investigated for restricting the number of potential schedules. Recombination and mutation operators are proposed, in order to use the new representations in evolutionary algorithms. These new representations are empirically compared to traditional representations using different network instances, one of them being a large and complex network from UK. By means of the new representations, the evolutionary algorithm developed during this thesis finds new best-known solutions for both networks. Pump scheduling as the multi-objective problem of minimising energy and maintenance costs in terms of Pareto optimality is also investigated in this thesis. Two alternative surrogate measures of maintenance cost are considered: the minimisation of the number of pump switches and the maximisation of the shortest idle time. A single run of the multi-objective evolutionary algorithm obtains pump schedules with lower electrical cost and lower number of pump switches than those found in the literature. Alternatively, schedules with very long idle times may be found with slightly higher electrical cost. Finally, ant colony optimisation is also adapted to the pump scheduling problem. Both Ant System and Max-Min Ant System are tested. Max-Min Ant System, in particular, outperforms all other algorithms in the large real-world network instance and obtains competitive results in the smallest test network. Computation time is further reduced by parallel simulation of pump schedules.

Made available in DSpace on 2016-06-02T19:53:32Z (GMT). No. of bitstreams: 1 MARTINEZ_Jonathan_2014.pdf: 11989383 bytes, checksum: 96fb53d9544014ea55b1e53ee779c134 (MD5) Previous issue date: 2014-03-14 / Financiadora de Estudos e Projetos / This study presents a pump scheduling problem for the capture, transfer and storage of water supply systems in urban networks, whose objective is to minimize the electricity cost associated to the pumping operations. To deal with the dynamic and random nature of the water-demand, we propose two-stage stochastic programming with recourse models, where the random variables are represented by a finite and discrete set of realizations or scenarios. The developed mathematical models are extensions of previous deterministic models of the literature and they reflect the basic assumption that a fixed cost could be incurred by the turn on/ turn off activities of the hydraulic pumps. In order to control violations of the water-demand constraints in the presence of multiple different scenarios, we also consider a robustness technique in an attempt to obtain almost feasible solutions. Last, but not least, we adopt a risk-aversion criteria so-called mean absolute deviation to obtain second-stage costs less dependent on the realizations of the scenarios. The scenarios were generated according to a Monte-Carlo simulation procedure that may use any probability distributions to produce the empirical probabilities of the random variables. As the proposed pump scheduling problem with fixed cost is a two-stage stochastic mixed 0 &#8722; 1 program, we develop a efficient hybrid heuristic to obtain good-quality solutions of practical instances in a plausible running time. Overall results evidence the stability of the scenario generation method, the sensitivity of the solution according to the key parameters of the mathematical model, and the efficiency of the heuristic in solving large instances. Finally, we show that is possible to save resources by solving the stochastic programming model instead of adopting simpler approaches based on the expected value. / Esse estudo apresenta um problema de programação de bombas para a captação, armazenamento e transferência de água em sistemas de abastecimentos de água em redes urbanas, cujo objetivo é minimizar o custo de energia elétrica associado às operações de bombeamento. Para lidar com a natureza dinâmica e aleatória da demanda por água, foram propostos modelos de programação estocástica de dois estágios com recurso, em que a variável aleatória é representada por um conjunto finito de realizações ou cenários. Os modelos matemáticos desenvolvidos são extensões de modelos determinísticos da literatura e refletem a suposição básica de que é possível se incorrer em um custo fixo pelas atividades de liga/desliga das bombas hidráulicas. Para controlar as violações das restrições de demanda por água na presença de múltiplos cenários diferentes, considerou-se também uma técnica de robustez na tentativa de gerar soluções quase factíveis. Por último, mas não menos importante, adotou-se um critério de aversão ao risco denominado desvio médio absoluto para obter custos de segundo estágio menos dependentes das realizações dos cenários. Os cenários foram gerados de acordo com um procedimento baseado em simulação Monte-Carlo que pode utilizar qualquer distribuição de probabilidade para produzir as probabilidades empíricas das variáveis aleatórias. Como o problema de programação de bombas com custo fixo proposto é um programa inteiro misto 0&#8722;1 estocástico, desenvolve-se uma heurística híbrida eficiente para obter soluções de boa qualidade de instâncias práticas em um tempo computacional plausível. Os resultados evidenciam a estabilidade do método de geração de cenários, a sensibilidade da solução de acordo com parâmetros-chave do modelo matemático, e a eficiência da heurística na resolução de instâncias de grande porte. Finalmente, foi demonstrado que é possível poupar recursos pela resolução do modelo de programação estocástica, em vez de adotar abordagens mais simples baseadas no valor esperado.