Remote communities, characterized by no connection to the main power grid, traditionally get their power from diesel generators. Long geographical distances and lack of suitable roads make the fuel transportation difficult and costly, increasing the final cost of electricity. A microgrid using renewable energy as the main source can serve as a viable solution for this problem with considerable economical and environmental benefits. The focus of this research is to develop a microgrid for a remote community in northern Ontario (Canada) that combines wind, as a renewable source of energy, and a hydrogen-based energy storage system, with the goal of meeting the demand, while minimizing the cost of energy and adverse effect on the environment. The existing diesel generators remain in the system, but their use is minimized. The microgrid system studied in this research uses a wind turbine to generate electricity, an electrolyser to absorb the excess power from the wind source, a hydrogen tank to store the hydrogen generated by the electrolyser, a fuel cell to supply the demand when the wind resource is not adequate, and a diesel generator as a backup power. Two scenarios for unit-sizing are defined and their pros. and cons. are discussed. The economic evaluation of scenarios is performed and a cost function for the system is defined. The optimization problem thus formulated is solved by solvers in GAMS. The inputs are wind profile of the area, load profile of the community, existing sources of energy in the area, operating voltage of the grid, and sale price of electricity in the area. The outputs are the size of the fuel cell and electrolyser units that should be used in the microgrid, the capital and running costs of each system, the payback period of the system, and cost of generated electricity. Following this, the best option for the microgrid structure and component sizes for the target community is determined. Finally, a MATLAB-based dynamic simulation platform for the system under study with similar load/wind profile and sizing obtained in optimization problem is developed and the dynamic behaviour of microgrid at different cases is studied.

Combined cooling, heating and power (CCHP) systems are known as trigeneration systems, designed to provide electricity, cooling and heating simultaneously. The CCHP system has become a hot topic for its high system efficiency, high economic efficiency and less greenhouse gas (GHG) emissions in recent years. The efficiency of the CCHP system depends on the appropriate system configuration, operation strategy and facility size. Due to the inherent and inevitable energy waste of the traditional operation strategies, i.e., following the electric load (FEL) and following the thermal load (FTL), more efficient operation strategy should be designed. To achieve the highest system efficiency, facilities in the system should be sized to match with the corresponding operation strategy. In order to reduce the energy waste in traditional operation strategies and improve the system efficiency, two operation strategy design methods and sizing problems are studied (In Chapter 2 and Chapter 3). Most of the improved operation strategies in the literature are based on the ''balance'' plane, which implies the match of the electric demands and thermal demands. However, in more than 95% energy demand patterns, the demands cannot match with each other at this exact ''balance'' plane. To continuously use the ''balance'' concept, in Chapter 2, the system configuration is modified from the one with single absorption chiller to be the one with hybrid chillers and expand the ''balance'' plane to be a ''balance'' space by tuning the electric cooling to cool load ratio. With this new ''balance'' space, an operation strategy is designed and the power generation unit (PGU) capacity is optimized according to the proposed operation strategy to reduce the energy waste and improve the system efficiency. A case study is conducted to verify the feasibility and effectiveness of the proposed operation strategy. In Chapter 3, a more mathematical approach to schedule the energy input and power flow is proposed. By using the concept of energy hub, the CCHP system is modelled in a matrix form. As a result, the whole CCHP system is an input-output model. Setting the objective function to be a weighted summation of primary energy savings (PESs), hourly total cost savings (HTCs) and carbon dioxide emissions reduction (CDER), the optimization problem, constrained by equality and inequality constraints, is solved by the sequential quadratic programming (SQP). The PGU capacity is also sized under the proposed optimal operation strategy. In the case study, compared to FEL and FTL, the proposed optimal operation strategy saves more primary energy and annual total cost, and can be more environmental friendly. Finally, the conclusions of this thesis is summarized and some future work is discussed. / Graduate

Remote communities, characterized by no connection to the main power grid, traditionally get their power from diesel generators. Long geographical distances and lack of suitable roads make the fuel transportation difficult and costly, increasing the final cost of electricity. A microgrid using renewable energy as the main source can serve as a viable solution for this problem with considerable economical and environmental benefits. The focus of this research is to develop a microgrid for a remote community in northern Ontario (Canada) that combines wind, as a renewable source of energy, and a hydrogen-based energy storage system, with the goal of meeting the demand, while minimizing the cost of energy and adverse effect on the environment. The existing diesel generators remain in the system, but their use is minimized. The microgrid system studied in this research uses a wind turbine to generate electricity, an electrolyser to absorb the excess power from the wind source, a hydrogen tank to store the hydrogen generated by the electrolyser, a fuel cell to supply the demand when the wind resource is not adequate, and a diesel generator as a backup power. Two scenarios for unit-sizing are defined and their pros. and cons. are discussed. The economic evaluation of scenarios is performed and a cost function for the system is defined. The optimization problem thus formulated is solved by solvers in GAMS. The inputs are wind profile of the area, load profile of the community, existing sources of energy in the area, operating voltage of the grid, and sale price of electricity in the area. The outputs are the size of the fuel cell and electrolyser units that should be used in the microgrid, the capital and running costs of each system, the payback period of the system, and cost of generated electricity. Following this, the best option for the microgrid structure and component sizes for the target community is determined. Finally, a MATLAB-based dynamic simulation platform for the system under study with similar load/wind profile and sizing obtained in optimization problem is developed and the dynamic behaviour of microgrid at different cases is studied.

The research is motivated by the need to address two major challenges in wind power integration: how to mitigate wind power fluctuation and how to ensure stability of the farm and host grid. It is envisaged that wind farm power output fluctuation can be reduced by using a specific type of buffer, such as an energy storage system (ESS), to absorb its negative impact. The proposed solution, therefore, employs ESS to solve the problems. The key research findings include a new technique for calculating the desired power output profile, an ESS charge-discharge scheme, a novel direct-calculation (optimization-based) method for determining ESS optimal rating, and an ESS operation scheme for improving wind farm transient stability. Analysis with 14 wind farms and a compressed-air energy storage system (CAES) shows that the charge-discharge scheme and the desired output calculation technique are appropriate for ESS operation. The optimal ESSs for the 14 wind farms perform four or less switching operations daily (73.2%-85.5% of the 365 days) while regulating the farms output variation. On average, the ESSs carry out 2.5 to 3.1 switching operations per day. By using the direct-calculation method, an optimal ESS rating can be found for any wind farm with a high degree of accuracy. The method has a considerable advantage over traditional differential-based methods because it does not require knowledge of the analytical form of the objective function. For ESSs optimal rating, the improvement in wind energy integration is between 1.7% and 8%. In addition, a net increase in grid steady-state voltage stability of 8.3%-18.3% is achieved by 13 of the 14 evaluated ESSs. For improving wind farm transient stability, the proposed ESS operation scheme is effective. It exploits the use of a synchronous-machine-based ESS as a synchronous condenser to dynamically supply a wind farm with reactive power during faults. Analysis with an ESS and a 60-MW wind farm consisting of stall-regulated wind turbines shows that the ESS increases the farm critical clearing time (CCT) by 1 cycle for worst-case bolted three-phase-to-ground faults. For bolted single-phase-to-ground faults, the CCT is improved by 23.1%-52.2%. / text