The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

Our energy system is rapidly transforming, partially due to advances in internet and communications technologies that leverage an unprecedented amount of data. Industry proponents of the so-called “smart grid” suggest these technologies facilitate deeper engagement with end-users of energy (utility customers) that can in turn drive behavior-based changes and accelerate a renewable energy transition. While there has been progress in understanding how these technologies change consumer behavior using, for example, real-time feedback, it’s unclear how specific segments (e.g., renters) respond to these interventions; it’s also unclear why feedback is, or is not, producing changes in energy consumption. The literature suggests that behavioral strategies (e.g. information feedback, competitions, incentives) coupled with technology may present a way for utilities and efficiency programs to create savings—expanding opportunities for those often underserved by traditional approaches, such as renters—yet this coupling is not well understood, neither broadly (for all end users) nor specifically (for renters). This dissertation builds upon that literature and explores a human side of the smart grid, using a field experiment in renter households to test the interacting effects of real-time energy feedback and a novel form of financial incentive, referred to here as a competitive performance-based incentive. The experiment had two phases: phase one tested the feedback against a control group; phase two tested feedback, the incentive, and a combined treatment, against a control group. Results of these interventions were measured with pre- and post-treatment surveys as well as observed electricity consumption data from each household’s smart meter. The results of this experiment are described in three papers. Paper one examines the interventions’ individual and combined effectiveness at motivating renters to reduce or shift timing of electricity consumption. Feedback alone produced a significant savings effect in phase one. In phase two, the effect of the feedback wore off; the incentive alone had no significant effect; and the group that received feedback and the incentive experienced a doubling of savings relative to the effect of feedback alone, as observed in phase one. Paper two uses pre- and post-intervention survey data to examine how individual perceptions of energy change as a result of the interventions. Perception of large energy-using appliances changed the most in households that received feedback, suggesting that better information may lead to more effective behavior changes. Paper three leverages the results of the first two components to evaluate the policy implications and impacts on demand side management for utilities, efficiency programs, and the potential for behavior-based energy efficiency programs. Advocates of the smart grid must recognize the technology alone cannot produce savings without better engagement of end-users. Utility rate designers must carefully consider how time-based rates alone may over-burden those without the enabling technology to understand the impact of their energy choices.

In the context of the envisioned electric power delivery system of the future, the smart grid, this dissertation focuses on control and management strategies for integration of distributed energy resources in the power system. This work conceptualizes a hierarchical framework for the control of microgrids---the building blocks of the smart grid---and develops the notion of potential functions for the secondary control for devising intermediate set points to ensure feasibility of operation of the system. A scalar potential function is defined for each controllable unit of the microgrid such that its minimization corresponds to achieving the control goal. The set points are dynamically updated using communication within the microgrid. This strategy is generalized to (i) include both local and system-wide constraints and (ii) allow a distributed implementation. This dissertation also proposes and evaluates a simple yet elaborate distributed strategy to mitigate the transients of controllable devices of the microgrid using local measurements. This strategy is based on response monitoring and is augmented to the existing controller of a power system device. This strategy can be implemented based on either set point automatic adjustment (SPAA) or set point automatic adjustment with correction enabled (SPAACE) methods. SPAA takes advantage of an approximate model of the system to calculate intermediate set points such that the response to each one is acceptable. SPAACE treats the device as a generic system and monitors its response and modulates its set point to achieve the desired trajectory. SPAACE bases its decisions on the trend of variations of the response and accounts for inaccuracies and unmodeled dynamics. Case studies using the PSCAD/EMTDC software environment and MATLAB programming environment are presented to demonstrate the application and effectiveness of the proposed strategies in different scenarios.

In the context of the envisioned electric power delivery system of the future, the smart grid, this dissertation focuses on control and management strategies for integration of distributed energy resources in the power system. This work conceptualizes a hierarchical framework for the control of microgrids---the building blocks of the smart grid---and develops the notion of potential functions for the secondary control for devising intermediate set points to ensure feasibility of operation of the system. A scalar potential function is defined for each controllable unit of the microgrid such that its minimization corresponds to achieving the control goal. The set points are dynamically updated using communication within the microgrid. This strategy is generalized to (i) include both local and system-wide constraints and (ii) allow a distributed implementation. This dissertation also proposes and evaluates a simple yet elaborate distributed strategy to mitigate the transients of controllable devices of the microgrid using local measurements. This strategy is based on response monitoring and is augmented to the existing controller of a power system device. This strategy can be implemented based on either set point automatic adjustment (SPAA) or set point automatic adjustment with correction enabled (SPAACE) methods. SPAA takes advantage of an approximate model of the system to calculate intermediate set points such that the response to each one is acceptable. SPAACE treats the device as a generic system and monitors its response and modulates its set point to achieve the desired trajectory. SPAACE bases its decisions on the trend of variations of the response and accounts for inaccuracies and unmodeled dynamics. Case studies using the PSCAD/EMTDC software environment and MATLAB programming environment are presented to demonstrate the application and effectiveness of the proposed strategies in different scenarios.

Electric vehicles (EVs) offer an exciting opportunity in China both in terms of the potential to build a domestic manufacturing base and the potential to create a strong domestic market for the product. The Chinese nation stands to benefit from both supply-side and demand-side promotion due to the economic stimulus from EV manufacturing and export, the environmental benefits of reduced air pollution and reduced greenhouse gas emissions, and the energy security benefits of transitioning away from foreign oil dependence. The Chinese have several advantages when it comes to stimulating EV industry development and EV deployment, including: leadership in battery technology, great potential for cost competitiveness, an enormous and emerging number of new car buyers, and high level government support. Yet a number of challenges must be taken into account as well, including: shortfalls in overall automobile R&D spending, consumer concerns about Chinese cars’ safety and reliability, enhancing the appeal of the Chinese brand, and heavy national infrastructure demands. This paper will seek to examine the opportunities and challenges associated with EV deployment in China and identify industry actions and policy measures to facilitate the process.

Germany is to undertake a speedy phase-out of nuclear energy and at the same time move into the age of renewable energy. The policy basis for the transformation of the electricity system is the Renewable Energy Sources Act (EEG). The aim of this report is to investigate the transformation of the German electricity system: popularly called the Energiewende. The report will introduce and analyze the Renewable Energy Sources Act as a policy instrument, and how the electricity grid needs to be developed in order to handle the increasing shares of electricity from renewable sources. The history, main regulations, and the success of the EEG will be investigated. Furthermore, the ways in which the EEG needs to be revised will be given attention. The imperfections of today’s electricity grid when implementing a dominating share of renewable electricity, and ways in which Information and Communication Technology can be used in solving those imperfections will be analyzed. The basis for this thesis is a literature study. Since this is a current topic changing frequently, up-to-date research is used as the main reference. The EEG is based on a feed-in tariff system. The main concern when implementing a dominating share of renewable electricity is the fluctuation over time. It is difficult to know how much power will be produced and when. The future challenge of the electricity grid is to keep meeting demand and supply in a secure way. To succeed with the transformation, the EEG not only needs to be revised but a solution to the system stability is also necessary. The EEG is considered a successful policy instrument but what it is missing today is incentives for balancing demand and supply, energy efficiency, and technology innovation. In order to deal with fluctuating sources, the main focus when upgrading the grid should be to improve the forecasting issues. The success of making RES a significant part in electricity generation could become strong proof for the global community that an electricity system based on renewable energy sources is possible.

This report seeks to evaluate and predict possible power quality issues regarding Fortums engagement in the project of Stockholm Royal Seaport. Stockholm Royal Seaport is a city district planned by Stockholm Municipality to be constructed based on sustainable urban city principles. Fortum has, together with additional partners, engaged in the challenge to create a sustainable energy system. This is thought to be achieved through several measures. Energy saving actions are incorporated at several levels and there is a plan to create a “smart grid” for the electricity supply. A smart grid has no strict definition but in this case a key feature is “demand-response” which effectively means a way to optimize the consumption to have a more balanced consumption over the 24 hours of a day. One of the key components in the smart grid is the “active house” which is planned to have several specific features separating it from an ordinary house. It is planned to have its own contribution to electricity production using solar cells and an energy storage using batteries. Another feature is thought to be both automation and economic incentives measures to achieve peak load reduction. This thesis has taken the perspective of the end customer in the active house and has tried to evaluate the power quality to be experienced. An investigation regarding the different components has been carried out to get an overview from the mentioned perspective and identify possible problems or issues that may require attention in the realization of Stockholm Royal Seaport. It has been found that no major problems are to be expected but some smaller issues has arisen that might be worthwhile giving some attention.

The need for a more efficient electrical grid has made the smart grid concept popular in recent years. The aims of this study are to identify the conditions in Sweden for implementing a smart grid and to analyse the opinions of stakeholders. One finding is that the large capacity of Sweden’s existing electrical grid decreases the immediate need for smart grid solutions. However, the rapid increase in wind power might push the development of a smart grid in the coming years. By employing case study methodology, five different smart grid projects in Sweden are discussed, using different theoretical frameworks, including actor network theory, discourse analysis, technological trajectories, diffusion of innovation and timing of entry. Norra Djurgardsstaden, a large construction project in Stockholm in which smart grid technology is used, is then analysed. The differing views of the parties involved in the project raise the question of whether more coordination is needed. Another project at Falbygdens Energi focuses on energy storage in batteries. This project poses the question of whether the regulation of the Swedish power market needs to be reformed to support the smart grid and to encourage new ways of collaborating and doing business. The final discussion concludes by suggesting new research questions.

Traditionally, most consumers of electricity pay for their consumption according to a fixed-rate. The few existing implementations of real time pricing have been restricted to large industrial consumers, where the benefits could justify the high implementation cost. With the advancement of Smart Grid technologies, large scale implementation of variable-rate metering will be more practical. Consumers will be able to control their electricity consumption in an automated fashion, where one possible scheme is to have each individual maximize their own utility as a noncooperative game. In this thesis, noncooperative games are formulated among the consumers of Smart Grid with two real-time pricing schemes, where the Nash equilibrium operation points are investigated for their uniqueness and load balancing properties. The first pricing scheme charges a price according to the average cost of electricity borne by the retailer and the second charges according to a time-variant increasing-block price. The zero revenue model and the constant revenue rate model, are the two revenue models being considered. The relationship between these games and certain congestion games, known as atomic flow games from the computer networking community, is demonstrated. It is shown that the proposed noncooperative game formulation falls under the class of atomic splittable flow games. It is shown that the Nash equilibrium exists for four different cases, with different pricing schemes and revenue models, and is shown to be unique for three of the cases, under certain conditions. It is shown that both pricing schemes lead to similar electricity loading patterns when consumers are interested only in the minimization of electricity costs. Finally, the conditions under which the increasing-block pricing scheme is preferred over the average cost based pricing scheme are discussed.

Environmental concerns have led to a rapid increase in renewable energy development and production as the global demand for electricity continues to increase. The intermittent and uncertain nature of electricity generation from renewable sources, such as wind and solar, however, create significant challenges in maintaining power system reliability at reasonable costs. Energy storage and smart-grid technologies are perceived to provide potential solutions to these challenges in modern power systems of different sizes. This work investigates the opportunity to incorporate energy storage in microgrids with renewable energy production, as well as applying smart microgrid management techniques to reduce the lifetime costs while maintaining an acceptable level of reliability. A microgrid consisting of a 5 home community with generation supplied by two propane generators to meet the “N-1” reliability criterion is used as the base case scenario. Actual load data of typical homes is obtained from the industry partner. An equivalent loss of load expectation criterion is used to benchmark the acceptable reliability level. A model is developed to calculate the lifetime operational cost of the base case scenario which is used to assess the benefit of the addition of renewable energy sources, energy storage, and smart microgrid management techniques. A MATLAB program is developed to assess the 20 year operational costs of various combinations of renewable energy sources and battery energy storage, which will be considered the lifetime of the system. The combination of generation and storage which yields the lowest lifetime operational cost is defined as the optimized microgrid, and is used as a basis to determine if additional savings are realized by the implementation of a microgrid operated by a Smart Microgrid Management System (SMMS). The conceptual layout of the proposed SMMS is presented along with identified methods of utilizing in-home thermal storage. The SMMS mechanism is discussed along with proposed functionality, potential methods of employment, and associated development and implementation costs. The microgrid operated by the SMMS is assessed, and its lifetime operational cost is presented and contrasted against the base case microgrid and the optimized microgrid. A power system reliability evaluation of the proposed microgrids are conducted using a probabilistic method to ensure that reliability is not sacrificed by the implementation of a cost-minimized microgrid. A sequential Monte Carlo simulation model is developed to assess the power system reliability of the various microgrid configuration cases. The functionality of this model is verified using an existing reliability assessment program. The results from the presented studies show that the implementation of renewable energy sources, energy storage, and smart microgrid management techniques are an effective way of reducing the operational cost of a remote microgrid while increasing its power system reliability.