Optimal sizing and operation of pumping systems to achieve energy efficiency and load shifting

This dissertation presents a pumping system operation efficiency improvement solution that includes optimal selection and control of the water pump. This solution is formulated based on the performance, operation, equipment and technology (POET) framework. The focus is on the minimization of the operational energy cost. This efficiency improvement solution is divided into three stages in accordance with the operation category of the POET framework. The first stage is to select the optimal pump capacity by considering both energy efficiency and load shifting requirements. The second stage is to develop a flexible pump controlling strategy that combines and balances the contributions from energy efficiency and load shifting. The last stage is to improve the robustness of the control system using the closed-loop model predictive control approach. An optimal pump capacity selection model is formulated. In this model, additional capacity requirements for load shifting are considered along with the traditional energy efficiency requirements. By balancing the contributions from load shifting and energy efficiency, the operational energy cost can be reduced by up to 37%. An optimal pump control is formulated. The objective of this control model is to balance the energy efficiency and load shifting contributions during the operation and minimize the operational energy cost. This control model is tested under different operational conditions and it is compared to other existing control strategies. The simulation and comparison results show that the proposed control strategy achieves the lowest operational energy cost in comparison to other strategies. This optimal pump control model is further modified into the closed-loop model predictive control format to increase the robustness of the control system under operation uncertainties. A mixed integer particle swarm optimization algorithms is employed to solve the optimization problems in this research. AFRIKAANS : Hierdie verhandeling bied ’n verbeterde oplossing vir die operasionele doeltreffendheid van pompstelsels wat die optimale keuse en beheer van die waterpomp insluit. Hierdie oplossing is geformuleer op ’n raamwerk wat werkverrigting, bedryf, toerusting en tegnologie in ag neem. Die oplossing fokus op die vermindering van bedryfsenergie koste. Hierdie oplossing is onderverdeel in drie fases soos bepaal deur die bedryfskategorie gegrond op die bogenoemde raamwerk: Die eerste fase is die keuse van die optimale pompkapasiteit deur beide energiedoeltreffendheid en lasverskuiwing in ag te neem. Die tweede fase is om ’n buigbare pompbeheer strategie te ontwikkel wat ’n goeie balans handhaaf tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing. Die derde fase is om die stabiliteit van die beheerstelsel te verbeter deur gebruik te maak van ’n geslote-lus beheermodel met voorspellende beheer (Predictive Control). ’n Model vir die keuse van optimale pompkapasiteit is geformuleer. In hierdie model word vereistes vir addisionele pompkapasiteit vir lasverskuiwing sowel as vereistes in terme tradisionele energiedoeltreffendheid in ag geneem. Deur die regte verhouding tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing te vind kan ’n besparing van tot 37% op die energiekoste verkry word. Optimale pompbeheer is geformuleer. Die doel van die beheermodel is om die bydraes van energiedoeltreffendheid en lasverskuiwing te balanseer en om die bedryfsenergie koste te minimiseer. Hierdie beheermodel is getoets onder verskillende bedryfstoestande en dit is vergelyk met ander bestaande beheerstrategiee. Die simulasie en vergelyking van resultate toon dat die voorgestelde beheerstrategie die laagste bedryfsenergie koste behaal in vergelyking met ander strategiee. Hierdie optimale pomp beheermodel is verder aangepas in ’n geslote beheermodel met voorspellende beheerformaat om die stabiliteit van die beheerstelsel te verbeter onder onsekere bedryfstoestande. ’n Gemende heelgetal partikel swerm optimisasie (Mixed interger particle swarm optimization) algoritme is gebruik om die optimiseringsprobleme op te los tydens hierdie navorsingsoefening.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer EngineeringUnrestricte

Understanding and Modeling Residential Electricity Demand in India

Residential electricity demand arises from the need for households to meet various end-use energy services. This demand has seen consistent growth over the last decade currently accounting for close to a quarter of the total electricity consumption in the country. In developing economies like India this sector will also be a key contributor to future greenhouse emissions given that we are starting from a comparatively low base. But our understanding of this sector is still limited. To gain a better understanding of this space, we approach this problem in two parts. In the first part, we outline a methodology to design and conduct a representative survey by presenting the case study of a primary survey we conducted of Bengaluru. Using the survey, we model appliance ownership and usage patterns identifying key contributing end-use categories and variations in patterns of electricity consumption across households. Next, we develop a bottom-up, end-use model, disaggregated by regions, to project growth in end-use energy service categories. We identify growth in ownership of key appliances and changes in consumption driven by this growth. We model changes in consumption patterns at different regional disaggregation identifying key demand drivers. Based on model insights from the primary survey and national model, we identify key policy amendments and suggest some new policy directions to manage the growth of demand from the residential sector

Optimal control on rock winder hoist scheduling

This dissertation addresses the problem of optimally scheduling the hoists of a twin rock winder system in a demand side management context. The objective is to schedule the hoists at minimum energy cost taking into account various physical and operational constraints and production requirements as well as unplanned system delays. The problem is solved by first developing a static linear programming model of the rock winder system. The model is built on a discrete dynamic winder model and consists of physical and operational winder system constraints and an energy cost based objective function. Secondly a model predictive control based scheduling algorithm is applied to the model to provide closed-loop feedback control. The scheduling algorithm first solves the linear programming problem before applying an adapted branch and bound integer solution methodology to obtain a near optimal integer schedule solution. The scheduling algorithm also compensates for situations resulting in infeasible linear programming solutions. The simulation results show the model predictive control based scheduling algorithm to be able to successfully generate hoist schedules that result in steady state solutions in all scenarios studied, including where delays are enforced. The energy cost objective function is proven to be very effective in ensuring minimal hoisting during expensive peak periods and maximum hoisting during low energy cost off-peak periods. The algorithm also ensures that the hoist target is achieved while controlling all system states within or around their boundaries for a sustainable and continuous hoist schedule. CopyrightDissertation (MEng)--University of Pretoria, 2010.Electrical, Electronic and Computer EngineeringUnrestricte

Dynamic Demand Response in Residential Prosumer Collectives

This research aims at exploring how smart grid opportunities can be leveraged to ameliorate demand response practices for residential prosumer collectives, while meeting the needs of end-users and power grids. Electricity has traditionally been generated in centralized plants then transmitted and distributed to end-users, but the increasing cost-effectiveness of micro-generation (e.g. solar photovoltaics) is resulting in the growth of more decentralized generation. The term "prosumers" is commonly used to refer to energy users (usually households) who engage in small-scale energy production. Of particular interest is the relatively new phenomenon of prosumer collectives, which typically involve interactions between small-scale decentralized generators to optimize their collective energy production and use through sharing, storing and/or trading energy. Drivers of collective prosumerism include sustaining community identity, optimizing energy demand and supply across multiple households, and gaining market power from collective action. Managing power flows in grids integrating intermittent micro-generation (e.g. from solar photovoltaics and micro-wind turbines) presents a challenge for prosumer collectives as well as power grid operators. Smart grid technologies and capabilities provide opportunities for dynamic demand response, where flexible demand can be better matched with variable supply. Ideally, smart grid opportunities should incentivize prosumers to maximize their energy self-consumption from local supply while fairly sharing any income from trading surplus energy, or any loss of utility associated with altering energy demand patterns. New businesses are emerging and developing various products and services around smart grid opportunities to cater for the socio-technical needs of residential prosumer collectives, where technical energy systems overlap with social interactions. This research studies how emerging businesses are using smart grid capabilities to create dynamic demand response solutions for residential prosumer collectives, and how fairness can be adopted in solutions targeting those collectives. This research interweaves social and technical knowledge from literature to interpret the interactions and objectives of prosumer collectives in new ways, and create new socio-technical knowledge around those interpretations. Conducting this research involved using mixed research methods to draw on social science, computer science, and power systems. In the social stream of the research, semi-structured interviews were conducted with executives in businesses providing current or potential smart grid solutions enabling dynamic demand response in residential prosumer collectives. In the technical stream, optimization, computation and game theory concepts were used to develop software algorithms for integrating fairness in allocating shared benefits and loss of utility in collective settings. Interview findings show that new business models and prosumer-oriented solutions are being developed to support the growth of prosumer collectives. Solutions are becoming more software-based, and enabling more socially-conscious user choice. Challenges include dealing with power quality rather than capacity, developing scalable business models and adequate regulatory frameworks, and managing social risks. Automated flexibility management is anticipated to dominate dynamic demand response practices, while the grid is forecast to become one big prosumer community rather than pockets of closed communities. Additionally, the research has developed two software algorithms for residential collectives, to fairly distribute revenue and loss of utility among households. The algorithms used game theory, optimization and approximation algorithms to estimate fair shares with high accuracy using less computation time and memory resources than exact methods