In this thesis, a demand elasticity model is developed and tested for the dispatch of high voltage power systems and microgrids. The price obtained from dispatching a network in a base-case scenario is used as input to a price-elastic demand model. This demand model is then used to determine the price-responsive demand for the next iteration, assuming that the load schedule is defined a day-ahead. Using this scheme, trends for demand, hourly prices, and total operation costs for a system can be obtained to study the impact of demand response on unit commitment and dispatch of distributed energy resources. This way, the effect on the scheduling of dispatchable generators and energy storage systems can be analyzed with respect to price-elastic loads. The results for a test power system and a benchmark microgrid show that as the demand is more elastic, the longer it takes for the dispatch to converge to a final condition. The 24-hour model eventually converges to a steady state, with prices and costs at their lowest values for different scenarios, which is good for most system participants and desirable in a market environment, thus highlighting the importance of price-responsive loads in electricity markets.

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A smart grid is different from a traditional power system in that it allows incorporation of intelligent features and functions, e.g., meter reading, adaptive demand response, integration of distributed energy sources, substation automation, etc. All these intelligent features and functions are achieved by choosing appropriate communication technologies and network structures for the smart grid appropriately. The objective of this dissertation is to develop an AHP (analytic hierarchy process) - based strategy for demand curtailment allocation that is subject to communications and cyber security limitations. Specifically, it: (1) proposes an electrical demand curtailment allocation strategy to keep the balance between supply and demand in case of the sudden supply shortage; (2) simulates the operation of the proposed demand curtailment allocation strategy considering the impact from communication network limitations and simultaneous operations of multiple smart grid applications sharing the same communication network; and (3) analyzes the performance of the proposed demand curtailment allocation strategy when selected cyber security technologies are implemented. These are explained in more details below. An AHP-based approach to electrical demand curtailment allocation management is proposed, which determines load reduction amounts at various segments of the network to maintain the balance between generation and demand. Appropriate communication technologies and the network topology are used to implement these load reduction amounts down to the end-user. In this proposed strategy, demand curtailment allocation is quantified taking into account the demand response potential and the load curtailment priority of each distribution substation. The proposed strategy helps allocate demand curtailment (MW) among distribution substations or feeders in an electric utility service area based on requirements of the central load dispatch center. To determine how rapidly the proposed demand curtailment strategy can be implemented, the capability of the communication network supporting the demand curtailment implementation needs to be evaluated. To evaluate the capability of different communication technologies, selected communication technologies are compared in terms of their latency, throughput, reliability, power consumption and implementation costs. Since a number of smart grid applications share the same communication network, the performance of this communication network is also evaluated considering simultaneous operation of popular smart grid applications. Lastly, limitations of using several cyber security technologies based on different encryption methods - 3EDS (Triple Data Encryption Standard), AES (Advanced Encryption Standard), Blowfish, etc. - in deploying the proposed demand curtailment allocation strategy are analyzed. / Ph. D.

Intelligent IT-based solutions, often called Smart Grids, are considered to be the future balancers of renewable energy sources. One area within the Smart Grid concept is called demand response, which is focused on making customers more consumption flexible by making them more active in their consumption. In this thesis the aim is to analyze a business model by investigating the income potential for a demand response solution as well as its market potential. This has been done through literature studies, interviews and development of a computational model. The use of 5000 households with flexible consumption can provide a cost reduction of 17.2% or 2.4 million SEK for a balance responsible party during an average year. If the solution is used to make strategic bids on the regulation markets the study indicates that the largest potential for revenues lies within this strategy. Potential customers show a genuine interest in the solution, but are worried about implementation costs, product reliability and contract solutions. The business model needs further development to increase its reliability. The impact of using strategic bids optimally needs to be investigated, and a thorough market analysis would be of great help to answer the questions: how should tomorrow’s contracts look like and which actors will be present on tomorrow’s electricity market?

The objective of this work is to design and implement an architectural framework for a web-based demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeowners' preferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a web-based HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed web-based HEM system, which makes it possible for residential customers to participate in demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood. As one of the demand response (DR)-enabling technologies, the web services-based DR communication has been developed to enable households without smart meters or advanced metering infrastructure (AMI) to participate in a DR event via the HEM system. This implies that an electric utility can send a DR signal via a web services-enabled HEM system, and appropriate appliances can be controlled within each home based on homeowner preferences. The interoperability with other systems, such as utility systems, third-party Home Area Network (HAN) systems, etc., is also taken into account in the design of the proposed web services-based HEM system. That is, it is designed to allow interaction with authorized third-party systems by means of web services, which are collectively an interface for machine-to-machine interaction. This work also designs and implements device organization and interface for end-use appliances utilizing ZigBee Device Profile and Smart Energy Profile. Development of the Home Area Network (HAN) of appliances and the HAN Coordinator has been performed using a ZigBee network. Analyses of security risks for a web-based HEM system and their mitigation strategies have been discussed as well. / Master of Science

With the grasp of a smart grid in sight, discussions have shifted the focus of system security measures away from generation capacity; apart from modifying the supply side, demand may also be exploited to keep the system in balance. Specifically, Demand Response (DR) is the concept of consumer load modification as a result of price signaling, generation adequacy, or state of grid reliability. Implementation of DR mechanisms is one of the solutions being investigated to improve the efficiency of electricity markets and to maintain system-wide stability.  In a liberalized electricity sector, with a smart grid vision that is committed to market-based operation, end-users have now become the focal point of decision-making at every stage of the process in producing, delivering and consuming electricity. DR program implementation falls within the smart grid domain: a complex socio-technical energy system with a multiplicity of physical, economic, political and social interactions. This thesis thus employs both qualitative and quantitative research methods in order to address the ways in which residential end-users can become active DR flexibility providers in deregulated European electricity markets. The research focuses on economic incentives including dynamic pricing contracts, dynamic distribution price signals and the aggregation of load flexibility for participation in the various short-term electricity markets. / <p>The Doctoral Degrees issued upon completion of the programme are issued by Comillas Pontifical University, Delft University of Technology and KTH Royal Institute of Technology. The invested degrees are official in Spain, the Netherlands and Sweden, respectively.</p><p>QC 20160225</p> / Erasmus Mundus Joint Doctorate in Sustainable Energy Technologies and Strategies (SETS)

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Public transportation in rural America has existed for decades. Its providers are challenged with low populations and long distances in rural areas. Many of these rural transit providers have been in existence for many years, but ridership still remains low. These providers usually operate in a demand-response format, as opposed to large cities, where buses run on fixed routes. This research was conducted to see if any type of service improvements or enhancements could be found in order to increase ridership of demand-response transit service in rural Kansas. In order to determine if ridership of public transportation in rural Kansas can be increased, customer satisfaction surveys were conducted. One survey was distributed to current riders of demand-response systems, one survey distributed to non-riders of public transportation, and the last survey given to providers to obtain basic system information throughout Kansas. Ridership is significantly skewed toward the elderly, disabled, and those who either choose not to drive or are unable to drive. Those who do not fall into one of these three categories often do not use public transportation in rural areas. For most of the riders, public transportation is their only reliable method of mobility as they are transit dependent. Only 35% of the riders had a personal vehicle they could use to make the trip had public transportation not been available. Riders of demand-response transit systems in rural Kansas are pleased with the service provided as a whole. Non-riders are ambivalent toward demand-response transit service. They appreciate the fact that in many cases general public transportation service exist, but they are also generally unwilling to use it themselves. These are typically choice riders, and are unlikely to switch to demand-response transit due to their other mobility options. It was found that the more vehicles a person has access to in their household, the less knowledge they have about public transportation in their area. These people are content to use the vehicles they have, because it is more convenient than using public transportation in rural Kansas. Improvements to the provider’s system, like extending operating hours and days, along with implementing GIS-assisted scheduling may bring higher ridership. However, this may only increase the number of rides by the same current riders with few new riders grained. Increasing the usage of demand response ridership will continue to be a challenge in the future with the increasing number of elderly in the years to come.

Based on a previous empirical study of the effect of a residential demand response program in Sala, Sweden, thisproject investigated the economic consequences of consumer behaviour change after a demand-based time ofuse distribution tariff was employed. The economic consequences of consumers were proven to bedisadvantageous in terms of unit electricity price. Consumers could achieve more electricity bill saving throughstabilising their electricity consumption during peak hours, and this way bring least compromising of theircomfort level.In order to estimate the price elasticity of the studies demand response program, a new method of estimationprice elasticity was proposed. With this method, the intensity of demand response of the demand responseprogram was estimated in terms of price elasticity. Regression analysis was also applied to find out the priceincentives of consumer behaviour change. And the results indicated that the rise in electricity supply chargehardly contributes to load reduction, while the demand-based tariff constituted an advantageous solution on loaddemand management. However stronger demand response still

Demand participation is a basic ingredient of the next generation of power exchanges in electricity markets. A key challenge in implementing demand response stems from establishing reliable market frameworks so that purchasers can estimate the demand correctly, buy as economically as possible and have the means of hedging the risk of lack of supply. System operators also need ways of estimating responsive load behaviour to reliably operate the grid. In this context, two aspects of demand response are addressed in this study: scheduling and baseline estimation. The thesis presents a market clearing algorithm including demand side reserves in a two-stage stochastic optimization framework to account for wind power production uncertainty. The results confirm that enabling the load to provide reserve can potentially benefit consumers by reducing electricity price, while facilitating a higher share of renewable energy sources in the power system. Two novel methods, Bayesian Linear regression and Kernel adaptive filtering, are proposed for baseline load forecasting in the second part of the study. The former method provides an integrated solution for prediction with full accounting for uncertainty while the latter provides an online sequential learning algorithm that is useful for short term forecasting. / Graduate / 0544 / nimahtehrani@gmail.com

This thesis describes a new method to evaluate the value of wind power combined with domestic demand response. The thesis gives a brief overview of current domestic demand management programmes, and highlights the demand response and its current application. Such technology has conventionally been used for different purposes, such as frequency regulation, and to minimize the spot electricity prices in the market. The aim is to show whether such technology may become useful to make the renewables, and in particular wind power more interesting for investors. An assessment framework based on generation scheduling is developed to quantify the value of wind power. A further important aspect of value of wind power is the impact of intermittency on overall reliability of the system. This necessitates increasing the spinning reserve level which will increase the production cost. The changes in the spinning reserve level has been investigated in this thesis and it has been shown that how different forecasting errors may change the overall value of a windfarm over its lifetime. One of the most important aspects of a system containing demand response, is the availability of demand response. A load modelling package is developed to show the potential for demand response in a real system from domestic sector. With every increasing the concerns with regard to future of generation mix in Britain, this work has proposed over 72 scenarios for the future of generation mix in Britain and the impact of demand response to increase the value of wind power in 2020 has been investigated. The assessment framework is enhanced by showing that how the value of wind power combined with domestic demand response may change by changes in emission price, and cost of demand response. This will show the degree of feasibility of such system in which demand response is treated like a commodity.