Buildings contribute a significant part to the electricity demand profile and peak demand for the electrical utilities. The addition of renewable energy generation adds additional variability and uncertainty to the power system. Demand side management in the buildings can help improve the demand profile for the utilities by shifting some of the demand from peak to off-peak times. Heating, ventilation and air-conditioning contribute around 45% to the overall demand of a building. This research studies two strategies for reducing the peak as well as shifting some demand from peak to off-peak periods in commercial buildings: 1. Use of gas heat pumps in place of electric heat pumps, and 2. Shifting demand for air conditioning from peak to off-peak by thermal energy storage in chilled water and ice. The first part of this study evaluates the field performance of gas engine-driven heat pumps (GEHP) tested in a commercial building in Florida. Four GEHP units of 8 Tons of Refrigeration (TR) capacity each providing air-conditioning to seven thermal zones in a commercial building, were instrumented for measuring their performance. The operation of these GEHPs was recorded for ten months, analyzed and compared with prior results reported in the literature. The instantaneous COPunit of these systems varied from 0.1 to 1.4 during typical summer week operation. The COP was low because the gas engines for the heat pumps were being used for loads that were much lower than design capacity which resulted in much lower efficiencies than expected. The performance of equivalent electric heat pump was simulated from a building energy model developed to mimic the measured building loads. An economic comparison of GEHPs and conventional electrical heat pumps was done based on the measured and simulated results. The average performance of the GEHP units was estimated to lie between those of EER-9.2 and EER-11.8 systems. The performance of GEHP systems suffers due to lower efficiency at part load operation. The study highlighted the need for optimum system sizing for GEHP/HVAC systems to meet the building load to obtain better performance in buildings. The second part of this study focusses on using chilled water or ice as thermal energy storage for shifting the air conditioning load from peak to off-peak in a commercial building. Thermal energy storage can play a very important role in providing demand-side management for diversifying the utility demand from buildings. Model of a large commercial office building is developed with thermal storage for cooling for peak power shifting. Three variations of the model were developed and analyzed for their performance with 1) ice storage, 2) chilled water storage with mixed storage tank and 3) chilled water storage with stratified tank, using EnergyPlus 8.5 software developed by the US Department of Energy. Operation strategy with tactical control to incorporate peak power schedule was developed using energy management system (EMS). The modeled HVAC system was optimized for minimum cost with the optimal storage capacity and chiller size using JEPlus. Based on the simulation, an optimal storage capacity of 40-45 GJ was estimated for the large office building model along with 40% smaller chiller capacity resulting in higher chiller part-load performance. Additionally, the auxiliary system like pump and condenser were also optimized to smaller capacities and thus resulting in less power demand during operation. The overall annual saving potential was found in the range of 7-10% for cooling electricity use resulting in 10-17% reduction in costs to the consumer. A possible annual peak shifting of 25-78% was found from the simulation results after comparing with the reference models. Adopting TES in commercial buildings and achieving 25% peak shifting could result in a reduction in peak summer demand of 1398 MW in Tampa.

Purpose: The purpose of the study is to examine households attitudes and commitment to demand side flexibility as well as the various actors perceptions of the division of responsibilities in the electricity market and the information available to households. Research questions:                   – What are the main factors for private electricity customers to be able to contribute with demand side flexibility to the electricity system? – What challenges and opportunities can be identified with increased demand side flexibility? Method: The study was based on an abductive approach, where the collection of primary data is of a quantitative and qualitative nature. The qualitative part was collected through 9 semi-structured interviews with actors linked to the electricity market. Collection of quantitative data was done through a survey aimed at households with a total of 110 respondents. The results of the data collection have been analyzed thematically together with the theoretical framework. Conclusions: The main factors for increased demand flexibility are, firstly, a clear division of responsibilities between authorities, households and other players in the electricity market. Secondly, well-developed information channels are required that can be made possible through new technology. Finally, an electricity market is required that allows new actors such as an aggregator. The challenges include engaging households, currently low profitability for demand side flexibility and a set of regulations and tariffs that are lagging behind in development. The opportunities are future changes in the electricity price that speak in favor of increased profitability for demand side flexibility and a change to more dynamic and flexible electricity network tariffs.

This work deals with the energy management of belt conveyor systems (BCS) under various demandside management (DSM) programmes. The primary objective of this work is to model the energy consumption and energy related cost of operating troughed belt conveyor systems under different electricity pricing tariffs. This research is motivated by the increasing need for energy efficiency and energy cost reduction in the operation of BCS. This is as a result of technological improvements in BCS technology leading to increasingly longer belts being commissioned and as a result of rapidly rising electricity costs. An energy model derived from established industry standards is proposed for long conveyors. The newly proposed model uses a first-order partial differential equation (PDE) in order to capture the state of material on the belt. This new model describes the conveyor's power requirement using an equation with two parameters. A system identification set-up involving a recursive parameter estimating algorithm is simulated for measurements with varying degrees of noise. The results show that the proposed model estimates conveyor power and material delivered by long conveyors more accurately than the existing steady-state models. Downhill conveyors (DHCs) are important potential energy sources that can be tapped to improve the overall energy efficiency of BCSs. A generic optimisation model that is able to optimally schedule three configurations of BCS with DHC is proposed. The economic assessment of implementing dynamic braking and regenerative drives technology on downhill conveyors is undertaken with the help of the model. The assessment shows that combining regenerative drives and optimal operation of BCS with DHC generates energy savings that give attractive payback period of less than 5 years. A chance-constrained model predictive control (cc-MPC) algorithm is proposed for scheduling belt conveyor systems with uncertain material demand on the output storage. The chance-constraints are based on the modelling of material demand by a sum of known mean demand and, zero-mean and normally distributed random component. The cc-MPC algorithm is shown to produce schedules that give a smaller number and smaller magnitude of storage limit violations compared to normal MPC and chance-constrained optimal control algorithms. An equation that gives the amount of effective storage required to meet storage constraints for a given value of standard deviation is established. The optimal scheduling of BCS under the real-time pricing (RTP) tariff is considered. This study develops a methodology for establishing the economic value of price forecasting schemes for loads capable of load-shifting. This methodology is used to show that the economic benefit obtained from a forecast is highly dependent on the volatility of the electricity prices being predicted and not their mean value. The methodology is also used to illustrate why the commonly used indices mean absolute percentage error (MAPE) and root mean square error (RMSE) are poor indicators of economic benefit. The proposed index using Kendall's rank correlation between the actual and predicted prices is shown to be a good indicator of economic benefit, performing far better than RSME and MAPE. / Thesis (PhD)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

Many utilities implement the financial incentive plans in promoting their Demand-Side Management (DSM) programs. The plans are intended to reduce the customer investment cost for a high efficiency equipment option, so that to make the investment more attractive. Despite its potential to increase customer participation, the financial incentives could cause a considerable increase in program cost to the utility. An analysis of financial incentive impact on the utility DSM program is conducted in this thesis. The analysis uses the combination of the customer participation modeling and the cost-benefit analysis of a DSM program. A modeling of customer participation by a discrete choice model is presented. The model uses the logistic probability functions. The benefit and cost of DSM programs are explored to develop the analysis methodology. Two typical energy conservation options of DSM programs are taken for case studies to demonstrate the analysis. The analysis is also conducted to see the effect of financial incentives on the performance of DSM programs in a fluctuating marginal energy cost. The result of this research shows that the financial incentive could induce the customer participation, thus provide an increase of benefit and costs. However, this research also reveals that, in certain circumstances, the financial incentive may result in a decrease of net benefit due to significant increase of cost. These imply that utilities must carefully evaluate the financial incentive plan in their DSM programs, before the programs are implemented. / Master of Science

Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed. / <p>QC 20171011</p>

Yes / In the UK there is no real time retail market, and hence no real time retail electricity pricing. Therefore domestic electricity consumers in the UK pay electricity prices that do not vary from hour to hour, but are rather some kind of average price. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. The key question is whether we can evaluate energy management and renewable energy intervention in the behaviour of customers in real market terms. Currently only behaviour changes with respect to total consumption can be evaluated. Interventions cannot be defined for peak load behaviour. The effectiveness of the introduction of renewable energy is also hard to assess. Therefore, it is hard to justify introducing of renewable and demand side management at local community level, apart from when following government approved schemes, subsidies, and other initiatives. In this paper, a new criteria has been developed to help developers and planners of local residential communities to understand the cost of intervention, in order to evaluate where the load is when the prices are high.

Yes / The introduction of smart metering has brought more detailed information on the actual load profile of a house. With the ability to measure, comes the desire to control the load profile. Furthermore, advances in renewable energy have made the consumer to become supplier, known as Prosumer, who therefore also becomes interested in the detail of his load, and also his energy production. With the lowering cost of smart plugs and other automation units, it has become possible to schedule electrical appliances. This makes it possible to adjust the load profiles of houses. However, without a market in the demand side, the use of load profile modification techniques are unlikely to be adapted by consumers on the long term. In this research, we will be presenting work on scheduling of energy appliances to modify the load profiles within a market environment. The paper will review the literature on algorithms used in scheduling of appliances in residential areas. Whilst many algorithms presented in the literature show that scheduling of appliances is feasible, many issues arise with respect to user interaction, and hence adaptation. Furthermore, the criteria used to evaluate the algorithms is often related only to reducing energy consumption, and hence CO2. Whilst this a key factor, it may not necessarily meet the demands of the consumer. In this paper we will be presenting work on a novel genetic algorithm that will optimize the load profile while taking into account user participation indices. A novel measure of the comfort of the customer, derived from the standard deviation of the load profile, is proposed in order to encourage the customer to participate more actively in demand response programs. Different scenarios will also be tested. / This work was supported by the British Council and the UK Department of Business Innovation and Skills under GII funding for the SITARA project.

This dissertation presents a comprehensive investigation into the performance optimization of a smart energy system called the Integrated Community Energy and Harvesting (ICE-Harvest) system, designed to optimize energy utilization in dense communities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources such as combined heat and power units, boilers, heat pumps, short-term storage systems, and long-term storage system. The objective of this research is to develop an optimal operation strategy for the system, considering the coordination of its components to realize its full potential including achieving demand management while ensuring occupants' comfort, harvesting and sharing waste energy, and facilitating energy arbitrage and taking advantage of energy price fluctuations, among other benefits. For this aim, the study begins by formulating precise quasi-dynamic mathematical representations of the system, considering the physical and operational limitations to capture the system's intricacies. The resultant optimization problem is a mixed integer nonlinear programming model that commercial solvers could not solve. To make the nonlinear models more tractable and solvable, various mathematical techniques are employed to linearize them. It is worth noting that many of these formulations are original contributions to the field. Given the specific configuration of the system with components requiring short-term and long-term operation scheduling and the large-scale nature of the optimization problem, a decomposition algorithm is proposed that breaks down the problem into three sequential layers: long-term, short-term, and ultra-short-term. Each layer addresses specific planning horizons, time resolutions, and optimization models, enabling effective optimization of the system's operation. The proposed optimization algorithm offers an effective framework for planning and optimizing ICE-Harvest operation at various time horizons and resolutions. It demonstrates the system's flexibility in performing waste energy harvesting and sharing, demand management, and dynamic switching between energy carriers based on real-time prices. / Dissertation / Doctor of Philosophy (PhD) / This dissertation aims to develop an energy management system for an integrated smart energy system, called integrated community energy and harvesting (ICE-Harvest). The ICE-Harvest system is envisioned as the future of energy systems for dense com munities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources. The goal is to coordinate all the variables and assets so that the system’s capabilities in harvesting waste energy to offset the community’s thermal demands, performing demand management without affecting occupants’ comfort, and realizing energy arbi trage are realized. For this aim, a hierarchical decision-making framework is developed in which three sequential layers are integrated. The three layers determine the long term, short-term, and ultra-short-term optimal operation of the ICE-Harvest system. The layers are differentiated by their objective, planning horizon, time resolution, and optimization models.

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In my dissertation, I examine varied types of knowledge and how they contribute to innovation generation and selection at both the firm and the industry level using the emerging industry context of small satellites. My research is divided into three papers. In Paper One, I take a supply-demand perspective and examine how suppliers of technology—with their unique knowledge of science and technology—and users of technology—with their unique knowledge of demand—contribute to innovation generation and selection over the industry lifecycle. Results show that the contributions of suppliers and users vary based on unique aspects of innovation, such as novelty, breadth, and coherence – and also over the industry life cycle. In Paper Two, I study how firms overcome science-business tension in their pursuit of novel innovation. I examine unique aspects of knowledge: scientists' business knowledge and CEOs' scientific knowledge. I show that CEOs' scientific knowledge is an important driver of firms' novel pursuits and that this impact is higher when scientists do not have business knowledge. In the third paper, I further examine how scientists with high technological and scientific knowledge—i.e., star scientists—impact firm innovation generation and selection. With a focus on explorative and exploitative innovation, I develop theory on the boundary conditions of stars' impact on firm level outcomes. I propose that individual level contingencies—i.e., stage of employment—and organizational level contingencies—explorative or exploitative innovation—both facilitate and hinder stars' impact on firms' innovative pursuits. / Doctor of Philosophy / In my dissertation, I study innovation at both the firm level and the industry level using the emerging industry context of small satellites. My dissertation divides into three papers. In Paper One, I study unique aspects of innovation at the industry level taking a supply-demand perspective. Since novelty, breadth, and convergence of innovation are all important drivers of the emergence and evolution of industries, I examine how supply side or demand side actors contribute to unique aspects of innovation over the industry life cycle. Results suggest that both suppliers and users of technology make important contributions to innovation, however, their respective contributions vary to novelty, breadth, and convergence of innovation. This impact varies over the industry life cycle. In Paper Two, I study how firms pursue novel innovation as main creator of economic value for firms. Firms need both scientific and technological knowledge in their pursuit of novel innovation. However, firms often struggle to overcome science-business tensions. Focusing on CEOs and scientists as two main drivers of innovation, I study how CEOs’ scientific knowledge and scientists’ business knowledge help firms overcome business-science tension. Results suggest that the likelihood of firms’ novel pursuit is higher when CEOs have scientific knowledge and scientists do not have business knowledge. In Paper Three, I further examine how high-performing scientists—i.e., star scientists—impact explorative and exploitative innovation. I propose that the stage of employment of individuals and goal context of firms are important contingencies that impact how stars impact firm level innovation.