In this thesis, load scheduling problem for cogeneration plants is interpreted in the context of stochastic programming. Cogeneration (CHP) is an important technology in energy supply of many countries. Cogeneration plants are designed and operated to cover the requested time varying demands in heat and power. Load scheduling of cogeneration plants represents a multidimensional optimization problem, where heat and electricity demands, operational parameters and associated costs exhibit uncertain behavior. Cogeneration plants are characterized by their &lsquo / heat to power ratio&rsquo / . This ratio determines the operating conditions of the plant. However, this ratio may vary in order to adapt to the physical and economical changes in power and to the meteorological conditions. Employing reliable optimization models to enhance short term scheduling capabilities for cogeneration systems is an important research area. The optimal load plan is targeted by achieving maximum revenue for cogeneration plants. Revenue is defined for the purpose of the study as the sales revenues minus total cost associated with the plant operation. The optimization problem, which aims to maximize the revenue, is modeled by thermodynamic analyses. In this context, the study introduces two objective functions: energy based optimization, exergy-costing based optimization. A new method of stochastic programming is developed. This method combines dynamic programming and genetic algorithm techniques in order to improve computational efficiency. Probability density function estimation method is introduced to determine probability density functions of heat demand and electricity price for each time interval in the planning horizon. A neural network model is developed for this purpose to obtain the probabilistic data for effective representation of the random variables. In this study, thermal design optimization for cogeneration plants is also investigated with particular focus on the heat storage volume.

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Several demand side management (DSM) techniques and algorithms have been used in the literature. These algorithms show that by adopting DSM and Time-of-Use (TOU) price tariffs; electricity cost significantly decreases, and optimal load scheduling is achieved. However, the purpose of the DSM is to not only lower the electricity cost, but also to avoid the peak load even if the electricity prices low. To address this concern, this dissertation starts with a brief literature review on the existing DSM algorithms and schemes. These algorithms can be suitable for Direct Load Control (DLC) schemes, Demand Response (DR), and load scheduling strategies. \end{abstract} Secondly, the dissertations compares two of DSM algorithms to show the performance based on cost minimization, voltage fluctuation, and system power loss [see in Chapter 5]. The results show the importance of balance between objectives such as electricity cost minimization, peak load occurrence, and voltage fluctuation evolution while simultaneously optimizing the cost.

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. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / Unrestricted

In this dissertation, the concept of using domain specific language to develop errorree parallel asynchronous load scheduling protocols for distributed systems is studied. The motivation of this study is rooted in addressing the high cost of verifying parallel asynchronous load scheduling protocols. Asynchronous parallel applications are prone to subtle bugs such as deadlocks and race conditions due to the possibility of non-determinism. Due to this non-deterministic behavior, traditional testing methods are less effective at finding software faults. One approach that can eliminate these software bugs is to employ model checking techniques that can verify that non-determinism will not cause software faults in parallel programs. Unfortunately, model checking requires the development of a verification model of a program in a separate verification language which can be an error-prone procedure and may not properly represent the semantics of the original system. The model checking approach can provide true positive result if the semantics of an implementation code and a verification model is represented under a single framework such that the verification model closely represents the implementation and the automation of a verification process is natural. In this dissertation, a domain specific language based verification framework is developed to design parallel load scheduling protocols and automatically verify their behavioral properties through model checking. A specification language, LBDSL, is introduced that facilitates the development of parallel load scheduling protocols. The LBDSL verification framework uses model checking techniques to verify the asynchronous behavior of the protocol. It allows the same protocol specification to be used for verification and the code generation. The support to automatic verification during protocol development reduces the verification cost post development. The applicability of LBDSL verification framework is illustrated by performing case study on three different types of load scheduling protocols. The study shows that the LBDSL based verification approach removes the need of debugging for deadlocks and race bugs which has potential to significantly lower software development costs.

In this dissertation, we focus on multiple levels of optimized resource management techniques. We first consider a classic resource management problem, namely the scheduling of data-intensive applications. We define the Divisible Load Scheduling (DLS) problem, outline the system model based on the assumption that data staging and all communication with the sites can be done in parallel, and introduce a set of optimal divisible load scheduling algorithms and the related fault-tolerant coordination algorithm. The DLS algorithms introduced in this dissertation exploit parallel communication, consider realistic scenarios regarding the time when heterogeneous computing systems are available, and generate optimal schedules. Performance studies show that these algorithms perform better than divisible load scheduling algorithms based upon sequential communication. We have developed a self-organization model for resource management in distributed systems consisting of a very large number of sites with excess computing capacity. This self-organization model is inspired by biological metaphors and uses the concept of varying energy levels to express activity and goal satisfaction. The model is applied to Pleiades, a service-oriented architecture based on resource virtualization. The self-organization model for complex computing and communication systems is applied to Very Large Sensor Networks (VLSNs). An algorithm for self-organization of anonymous sensor nodes called SFSN (Scale-free Sensor Networks) and an algorithm utilizing the Small-worlds principle called SWAS (Small-worlds of Anonymous Sensors) are introduced. The SFSN algorithm is designed for VLSNs consisting of a fairly large number of inexpensive sensors with limited resources. An important feature of the algorithm is the ability to interconnect sensors without an identity, or physical address used by traditional communication and coordination protocols. During the self-organization phase, the collision-free communication channels allowing a sensor to synchronously forward information to the members of its proximity set are established and the communication pattern is followed during the activity phases. Simulation study shows that the SFSN ensures the scalability, limits the amount of communication and the complexity of coordination. The SWAS algorithm is further improved from SFSN by applying the Small-worlds principle. It is unique in its ability to create a sensor network with a topology approximating small-world networks. Rather than creating shortcuts between pairs of diametrically positioned nodes in a logical ring, we end up with something resembling a double-stranded DNA. By exploiting Small-worlds principle we combine two desirable features of networks, namely high clustering and small path length.

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Growing concerns over global warming and increasing fuel costs have pushed the traditional fuel-based centralized electrical grid to the forefront of mounting public pressure. These concerns will only intensify in the future, owing to the growth in electricity demand. Such growths require increased generation of electricity to meet the demand, and this means more carbon footprint from the electrical grid. To meet the growing demand economically by using clean sources of energy, the electrical grid needs significant structural and operational changes to cope with various challenges. Microgrids (µGs) can be an answer to the structural requirement of the electrical grid. µGs integrate renewables and serve local needs, thereby, reducing line losses and improving resiliency. However, stochastic nature of electricity harvest from renewables makes its integration into the grid challenging. The time varying and intermittent<br>nature of renewables and consumer demand can be mitigated by the use of storages and dynamic load scheduling. Automated dynamic load scheduling constitutes the operational changes that could enable us to achieve energy efficiency in the grid.<br>The current research works on automated load scheduling primarily focuses on scheduling residential and commercial building loads, while the current research on manufacturing scheduling is based on static approaches with very scarce literature on job shop scheduling. However, residential, commercial and, industrial sector, each contribute to about one-third of the total electricity consumption. A few research<br>works have been done focusing on dynamic scheduling in manufacturing facilities for energy efficiency. In a smart grid scenario, consumers are coupled through electricity<br>pool and storage. Thus, this research investigates the problem of integrating production line loads with building loads for optimal scheduling to reduce the total electricity<br>cost in a µG.<br>This research focuses on integrating the different types of loads from different types of consumers using automated dynamic load scheduling framework for sequential decision making. After building a deterministic model to be used as a benchmark, dynamic load scheduling models are constructed. Firstly, an intelligent algorithm is developed for load scheduling from a consumer’s perspective. Secondly, load scheduling model is developed based on central grid controller’s perspective. And finally, a reinforcement learning model is developed for improved load scheduling by sharing<br>among multiple µGs. The performance of the algorithms is compared against different well-known individualistic strategies, static strategies and, optimal benchmark<br>solutions. The proposed dynamic load scheduling framework is model free with minimum assumptions and it outperforms the different well-known heuristics and static strategies while obtains solutions comparable to the optimal benchmark solution.<br>The future electrical grid is envisioned to be an interconnected network of µGs. In addition to the automated load scheduling in a µG, coordination among µGs by<br>demand and capacity sharing can also be used to mitigate stochastic nature of supply and demand in an electrical grid. In this research, demand and resource sharing<br>among µGs is proposed to leverage the interaction between the different µGs for developing load scheduling policy based on reinforcement learning. <br>