| Climate change is one of the most important issues in today's world, and there is an increasing concern about it. State of California is the leading states in the United States in cutting greenhouse gas (GHG) emissions; it sets an objective of achieving 1990 emission level by 2020 and also long-term objective of emission reduction to 80% of 1990 level by 2050. Short-term policies and necessary steps to take in the short term are well-defined, and California is on the right track of achieving 2020 goal.;There are many energy models developed for California. However, they either do not address cost implications of GHG mitigation in California or lack system modeling approach. These models cannot analyze abatement costs explicitly, consider interactive policies between different sectors of the economy or optimally allocate financial/physical resources. CA-TIMES is the first model that explicitly calculates the cost of mitigation, taken into account different sectors of energy system and their interactions and finds the optimal allocation of money/resources to reach policy target.;The residential and commercial sectors are modeled based on projected energy service demands that are independent of technology and fuels. The residential sector consists of end-use demand technologies used to satisfy thirteen residential end-use service demand, including space heating, space cooling, water heating, lighting, cooking, refrigeration, clothes washing, clothes drying, dish washing, freezer, TV, pool pumps, and miscellaneous. Likewise, the commercial sector end-use demand technologies comprise cooking, lighting, water heating, refrigeration, space cooling and heating, ventilation, office equipment and miscellaneous which are used in our model to satisfy service demand. The model is described by fuel types (e.g. natural gas, electricity, LPG) and end-use technologies (e.g. compact fluorescent lamps, furnace, TV) that meet these service demands. The energy service demands are projected based on assumed drivers that are population, building size, building heating/cooling coefficient, appliance saturation rate, appliance utilization rate and commercial floorspace. Future technology adoption and abatement rely on economic factors (including fuel price changes), consumer choices, technology availability, and policy choices to determine the total state-wide residential and commercial energy use over the time horizon. The model selects technologies to meet energy service demand while minimizing net system cost and satisfying other user-defined constraints such as policy goals in GHG emission targets, appliance efficiency standards, etc. The BAU policy assumes the existing policies do not expire, and they will continue throughout the modeling period until 2050. In the GHG reduction scenario, it is assumed there is a linear carbon cap constraint in addition to the available policies in the BAU scenario. The linear carbon cap assumes the carbon cap would be a straight-line trajectory from 2020 to 2050. CA-TIMES also can be used as a partial equilibrium model. Meaning that service demands are not fixed, and they can be changed based on the elasticities of service demands to their price each year. Under this framework, the model minimizes the welfare loss associated with the change in the service demand. The GHG reduction scenario that runs under this framework is called the GHG-Elastic demand scenario.;The residential and commercial sectors show substantial efficiency improvements and reductions in the final energy demand due to the adoption of more efficient technologies as well as technologies that rely on electricity more than natural gas. In 2010, electricity accounted for 57% of commercial energy use and 37% of residential energy consumption. By 2050, electricity's share of final energy is 67% in the commercial sector and 79% in the residential sector under the GHG reduction scenario. Overall, weighted efficiency for commercial and residential sectors is 2.3 and 3.89 times higher in 2050 relative to 2010 in the GHG reduction scenario, respectively. The model can reduce service demand instead of adopting efficient appliances to decrease GHG emissions in the GHG-Elastic demand scenario, which also lead to significant cost saving. So, weighted efficiency improvement for commercial and residential sectors in the GHG-Elastic demand scenario reduces to 2.21 and 3.55 in 2050, respectively. The model do not invest in ground source heat pumps, efficient electric water heaters and other efficient technologies, which are also expensive, to decrease GHG emissions. Instead, the model reduces service demand in various service demands to decrease emissions and abatement costs.;Electrification of buildings is interconnected with the increased demand for more low-carbon electricity generation. Under GHG scenarios, carbon intensity of electricity is decreased by 96% in 2050 relative to 2010.;Therefore, it is crucial to decarbonize the electricity through extensive use of renewables and design proper policies to promote efficiency improvement and reduce service demands to reach 2050 emissions reduction target with relatively low cost. (Abstract shortened by UMI.). |
