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Utility providers are under constant pressure to meet the ever-increasing demand for energy with a finite production capacity. Due to this, utility providers need to ensure that the demand does not exceed the supply. The use of demand response programs has been used as a solution to better match the available supply to the demand experienced on the grid. While demand response programs may improve the relationship between the supply and the demand, increasing consumer participation can further improve the effectiveness of demand response programs. The level of consumer participation is highly dependent on the incentives offered and the convenience of participating in the demand response program. However, most of these programs require consumers to actively monitor the available services and take appropriate action on their appliances. Home energy management systems attempt to provide convenience to consumers as well as increase their participation in demand response programs. They do this by enabling autonomous control and remote control of appliances. In addition, a home gateway makes it possible for the consumer and utility provider to interact with devices in the home remotely. Current solutions host the home energy management software on a home gateway or on a dedicated device in the home. Furthermore, these systems make use of proprietary software and devices to monitor and control the home environment. However, proprietary systems are costly to implement and maintain due to their dependence on the developers and the varying needs of consumers. This dissertation proposes a home energy management system solution that makes use of a standardized distributed machine-to-machine (M2M) middleware framework to support heterogeneous devices, technologies and protocols. In addition, the proposed solution shifts the software logic of the home energy management system from the gateway to a private cloud. This allows the system to leverage the benefits of virtualization and cloud computing such as cost efficiency, faster deployment and easier maintenance of the system. The proposed solution was implemented using a European Telecommunications Standards Institute (ETSI) compliant distributed M2M middleware framework (i.e. OpenMTC) and an open-source private cloud platform (i.e. OpenStack). A prototype was developed and tested with demand response programs that included a control demand response (DR) service, a consumption monitoring service and a time-of-use (ToU) service. In addition, the prototype made use of various third party applications, protocols and devices to support the aforementioned demand response services and provide automated and remote control of home appliances. Finally, an evaluation of the proposed system was conducted and concluded that the number of home energy management systems subscribed to the defined services influenced the effectiveness of these services because of the additional processing that the utility provider is required to perform.

The impact of distributed energy resources (DERs), electric vehicles/plug-in hybrid electric vehicles (EVs/PHEVs), and smart appliances on the distribution grid has been expected to be beneficial in terms of environment, economy, and reliability. But, it can be more beneficial by implementing smart controls. In the absence of additional controls, a negative effect was identified regarding the service lifetime of power distribution components. This research presents a new class of a smart house energy management system that can provide management and control of a residential house electric energy without inconvenience to the residents of the house and without overloading the distribution infrastructure. The implementation of these controls requires an infrastructure that continuously monitors the house power system operation, determines the real-time model of the house, computes better operating strategies over a planning period of time, and enables control of house resources. The smart house energy management system provides benefits for the good of utility and customer. In case of variable electricity rates, the management system can reduce the customer’s total energy cost. The benefits can be also extended to provide ancillary services to the utility such as control of peak load and reactive power support– assuming that this is worked out under a certain mutually beneficial arrangement between the utility and customer.

In this dissertation, we develop and analyze effective energy management policies for wireless sensor networks in emerging applications. Existing methods in this area have primarily focused on energy conservation through the use of various communication techniques. However, in most applications of wireless sensor networks, savings in energy come at the expense of several performance parameters. Therefore it is necessary to manage energy consumption while being conscious of its effects on performance. In most cases, such energy-performance issues are specific to the nature of the application. Our research has been motivated by new techniques and applications where efficient energy-performance trade-off decisions are required. We primarily study the following trade-off cases: energy and node replacement costs (Case I), energy and delay (Case II), and energy and availability (Case III). We consider these trade-off situations separately in three distinct problem scenarios. In the first problem (Case I), we consider minimizing energy and node replacement costs in underwater wireless sensor networks for seismic monitoring application. In this case, we introduce mixed-integer programming (MIP) formulations based on a combined routing and node replacement policy approach and develop effective policies for large problem instances where our MIP models are intractable. In the second problem (Case II), we develop a Markov decision process (MDP) model to manage energy-delay trade-off in network coding which is a new energy-saving technique for wireless networks. Here we derive properties of the optimal policy and develop in- sights into other simple policies that are later shown to be efficient in particular situations. In the third problem (Case III), we consider an autonomous energy harvesting sensor network where nodes are turned off from time to time to operate in an “energy-neutral” manner. In this case, we use stochastic fluid-flow analysis to evaluate and analyze the availability of the sensor nodes under effective energy management policies. In each of the above problem cases, we develop analytical formulations, and derive and/or analyze policies that effectively manage the considered energy-performance trade-off. Overall, our analyses and solution methods make new contributions to both operations research and communication networking literature.

The cost and fuel economy of hybrid electrical vehicles (HEVs) are significantly dependent on the power-train energy storage system (ESS). A series HEV with a minimal all-electric mode (AEM) permits minimizing the size and cost of the ESS. This manuscript, pursuing the minimal size tactic, introduces a bandwidth based methodology for designing an efficient ESS. First, for a mid-size reference vehicle, a parametric study is carried out over various minimal-size ESSs, both hybrid (HESS) and non-hybrid (ESS), for finding the highest fuel economy. The results show that a specific type of high power battery with 4.5 kWh capacity can be selected as the winning candidate to study for further minimization. In a second study, following the twin goals of maximizing Fuel Economy (FE) and improving consumer acceptance, a sports car class Series-HEV (SHEV) was considered as a potential application which requires even more ESS minimization. The challenge with this vehicle is to reduce the ESS size compared to 4.5 kWh, because the available space allocation is only one fourth of the allowed battery size in the mid-size study by volume. Therefore, an advanced bandwidth-based controller is developed that allows a hybridized Subaru BRZ model to be realized with a light ESS. The result allows a SHEV to be realized with 1.13 kWh ESS capacity. In a third study, the objective is to find optimum SHEV designs with minimal AEM assumption which cover the design space between the fuel economies in the mid-size car study and the sports car study. Maximizing FE while minimizing ESS cost is more aligned with customer acceptance in the current state of market. The techniques applied to manage the power flow between energy sources of the power-train significantly affect the results of this optimization. A Pareto Frontier, including ESS cost and FE, for a SHEV with limited AEM, is introduced using an advanced bandwidth-based control strategy teamed up with duty ratio control. This controller allows the series hybrid’s advantage of tightly managing engine efficiency to be extended to lighter ESS, as compared to the size of the ESS in available products in the market.

Huge amounts of energy are consumed by hospitals to improve the health environment for patients. This energy needs to be stable and continuous. Therefore, it is necessary to manage energy consumption. The purpose of this research is to study the energy management performance in hospitals in Saudi Arabia aiming to develop theoretical framework for energy management and to provide guideline to support implementing an effective energy management system in hospitals. This research consists of two main parts. The first part involves the assessment of the level of energy management program and the gathering of energy consumption quantitative data from five hospitals in order to determine the level of implementation of energy management program and the amounts and percentage of annual change in electricity consumption per bed in hospitals. The second part involves the inspection of the opinions of hospital top technical managers regarding energy management in their hospitals and their knowledge in auditing procedures, energy efficiency barriers, awareness to energy management and percentage of expected amount of saved energy. The research delivers recommendations tailored to the health sector in Saudi Arabia that encourages implementing energy management programs in hospitals to save energy, increase energy efficiency and improve energy management awareness.

A novel model was developed to test the use of short data sets for testing various alarm thresholds as part of an energy management program. Several years of 15-minute interval data were utilized from five buildings in Jacksonville, Florida. The model aggregated the data by day type and occupancy so that there were four period types used. For all of the buildings’ meters, their daily usage by period type was tested against the threshold to determine if an alarm would be triggered, which would then be assigned a reward and cost based upon the type and duration of response. The risk management value was converted to dollars, in order to normalize the energy and time. It was determined that the 5-month short data set was the most appropriate choice for short data sets. In addition, it was concluded that the thresholds should be set between 0.8 and 1.0 standard deviation above the average of the short window. Several recommendations for further study are also enclosed.

The goal to satisfy the energy demand, global warming and other environmental effects has prompted the urgency to shift energy generation systems to more affordable and sustainable methods, with the goal of phasing out the traditional-conventional systems to environmental friendly and sustainable generation systems. This campaign also calls for a more energy conscious society aware of its energy demand and promoting energy efficiency so as to minimize the demand through reduced wastages. For the later to be achieved energy systems should be understood in organization and this involves performing energy audits. This paper discusses the energy audit of Nchanga mine, Nchanga mine is located in Chingola town in the Copperbelt province of Zambia, and has operations involving underground mining, copper leaching, open pit mining and concentrate extraction. The Objective of the study was to perform a preliminary Audit; to understand the energy scenario and identify areas were savings could be realized. The problem however was that constraints on time could not allow a detailed analysis hence the scope was strictly on a preliminary audit study with minimal details on economics of measures and no analysis of investment cost. The method used for this analysis was the top down approach and the processes were divided into support and production processes to establish the energy balance. Nchanga mine has an annual energy use of about 656 GWh with an average maximum demand of about 88.6 MW. The total cost was about US. $ 34 million with maximum demand (MD) covering about $ 22 million and the rest was energy costs. This was obtained from analysis of energy bills from April 2011 to March 2012. The energy measures analyzed were according to the cost; cost influenced and non-cost influenced. The non-cost influenced measures gave a saving potential of about 15.6 MW ($ 3.9 million) on MD and 46 GWh ($ 818,800) on energy, giving a total electricity cost saving of about $ 4.7 million. The cost influenced measures gave a saving potential of about 12.7 MW ($ 3.1 million) on MD and 12 GWh ($ 213,600) on energy, giving a total electricity cost saving of about $ 3.4 million. These measures have a potential to reduce the overall MD by 28 MW (32% reduction), realizing an annual saving of about $ 7 million, and the overall energy use by about 58 GWh (8.8% reduction), which is an annual saving of about $ 1 million. This gives the total saving on the electricity bill of about $ 8 million that is about 24% reduction on the electricity bill with regards to last year’s expenditure.

Energy accounting determines how much a software process contributes to the total system energy consumption. It is the foundation for evaluating software and has been widely used by operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track every hardware use by software in a heterogeneous multicore system like modern smartphones and tablets. This work provides the ground truth for energy accounting based on multi-player game theory and offers the first evaluation of existing energy accounting policies, revealing their important flaws. The proposed ground truth is based on Shapley value, a single value solution to multi-player games of which four axiomatic properties are natural and self-evident to energy accounting. This work further provides a utility optimization formulation of energy management and shows, surprisingly, that energy accounting does not matter for existing energy management solutions that control the energy use of a process by giving it an energy budget, or budget based energy management (BEM). This work shows an optimal energy management (OEM) framework can always outperform BEM. While OEM does not require any form of energy accounting, it is related to Shapley value in that both require the system energy consumption for all possible combination of processes under question. This work reports a prototype implementation of both Shapley value-based energy accounting and OEM based scheduling. Using this prototype and smartphone workload, this work experimentally demonstrates how erroneous existing energy accounting policies can be, show that existing BEM solutions are unnecessarily complicated yet underperforming by 20% compared to OEM.

Riksbyggen, one of Sweden’s largest real estate companies, are planning to build a new residential area outside Uppsala, Sweden. Adjacent to the houses in the area, Riksbyggen are considering installing a 137 kWp photovoltaic (PV) system to supply the residents with renewable energy. In the first part of this study, the proposed PV system is analyzed based on benefits and profitability for the residents, using the software HOMER. Possibilities for including 3 kWp roof-mounted PV systems with varying azimuth as a complement to the larger system are evaluated. In the second part, options for implementing an Energy Management System (EMS) with battery storage or load shifting in the distribution grid using MATLAB. The system uses forecasting of PV generation, electricity prices and electricity demand to optimize the system control strategies. The results from the study indicate that installation of the 137 kWp PV system could lead to reduced average electricity costs of 21,1 % for the residents. If the system is completed with roof-mounted PV modules of varying azimuth for 12 % of the households in the area, the average electricity cost is reduced by 29,4 %in total. Implementing an EMS with energy storage in the distribution grid was not found to be an economically viable option, mainly due to high energy storage costs. The system control strategies can be improved by using forecasting. The economic benefits of load shifting for a single household were found to be too low to give incentive for system investment.