Wind-electricity is unpredictable in both intensity and duration. This thesis presents the design and implementation of Client-pull and Server-push architectures for the distribution of wind-electricity to Electrical Thermal Storage (ETS) units to match the electrical load of the ETS units with the electricity generation levels. Wind as an energy source is reviewed and the smart grid concept of a communication layer for the transmission, production and usage of electricity is explored. ETS operation is explained and a survey of the Client-pull and Server-push concepts. These implementations are evaluated on their ability to dispatch wind-electricity over a full heating season, short term latency, single day performance and complexity. Client-pull and Server-push architectures have nearly identical performance over a full heating season and identical performance over the 24 hour period evaluated. The Server-push architecture has lower short-term latency but is more complex than the Client-pull.

The optimum configuration of rotor-to-generator size for wind turbines is dependent upon the wind resource and is the configuration that produces the most electrical energy at a fixed capital cost. This optimization study held the combined cost of the rotor plus generator constant, but varied the respective sizes of the rotor and generator within this constraint. Total annual electrical energy was computed for each configuration at a series of wind resources each defined by a different Weibull probability distribution. In each case the configuration that produced the most electrical energy was determined to be the optimum. The fixed capital cost was also varied to see the effect on the optimum at each wind resource. It was found that the optimal rotor-to-generator size decreased as the average wind speed at a resource increased, and increased as Weibull shape parameter k increased. The optimal rotor-to-generator size decreased at a constant wind resource as the fixed capital cost increased. In each case there was a corresponding optimal capacity factor which never exceeded 0.5. Capacity factors above this optimum resulted in less electrical energy being produced for the same capital cost. The final product of the study is a series of graphs showing the optimum rotor size for a given generator size at a series of wind resources.

Recently, electric vehicles (EVs) have been gaining attention in passenger transportation due to their greater fuel economy and reduced greenhouse gas (GHG) emissions compared to conventional vehicles (CVs). The amount of GHG emissions reduction from EVs depends on the energy sources used to generate electricity. Wind is a clean, renewable energy source and EVs charged from wind-generated electricity do not produce any emissions. However, wind is variable in nature. This thesis examines the potential impact of EVs on reducing a jurisdiction’s vehicular GHG emissions using locally available wind-electricity. Four methods of charging EVs using wind-electricity are considered, with grid-electricity as a backup, and the overall well-to-wheels GHG emission reductions are discussed. The thesis includes a case study of Summerside. The results show that up to 68% of the EVs’ demands were met with wind-electricity, and Summerside’s vehicular GHG emissions were reduced by between 56% and 73% when compared to CVs.

Worldwide, wind energy added to the energy mix of electricity suppliers may be seen as way of improving energy security and reducing greenhouse gas emissions. However, due to wind's variability wind electricity cannot be used to meet demands which require a continuous supply of electricity. One solution to the variability problem is to adopt services that are capable of storing energy for use at a later time. Five new wind-allocation methods are considered to maximize its use of wind-electricity while at the same time reducing emissions. Simulations results, show that households benefit from an annual savings of about 30% to 36% with an estimated payback period ranging between 3.5 and 5.5 years. Emissions reduction in the off-peak scenarios is between 32% and 35% and about 86% in the anytime scenario. Heating demands satisfied ranges between 75% and 96% and total wind used for heating is between 3%-4%.

Wind power is the fastest growing sector of electricity generation in the world and the development of offshore wind resources is an increasingly important component of this growth. While more than 1.5GW have been installed in Europe and China, no turbines have been installed in United States waters even though several have been planned. Offshore wind power development in the United States must contend with significant challenges. There are numerous technical considerations including geological issues and undefined environmental conditions that affect the determination of appropriate design loads. Technological advancements are needed, and logistical questions must be addressed. The regulatory structure can be confusing and most permitting frameworks are not well established. Offshore wind projects are capital intensive and concerns exist that the industry will not be able to achieve a suitable economy of scale. Additionally, concerns about offshore wind impacts cross many areas such as the environment, visual and cultural concerns, navigational issues, and competing uses. This research project examines the technical issues of American offshore wind power and models basic project costs to provide an estimate of the total net present value for hypothetical utility-scale offshore wind projects in the United States. Costs have been examined by building a cost model and employing traditional cash flow analysis, regression, design of experiments, and random sampling techniques. / text