Incorporating Renewable Energy Sources into Electricity Grids: Challenges for Energy Systems Modeling

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In Canada, 38% of CO2 emissions come from 343 large emitters, of which 14 are coal-fired power plants that account for 12% of total CO2e emissions. At a global level, 45% or more of the electricity generated in China, Australia, India, the U.S. and Germany comes from coal. Although coal's share of primary energy is projected to decline from 30% in 2012 to 27% by 2035, coal demand will inevitably increase because overall energy demand (especially for electricity) is projected to increase by 41% over the same period. Since the public opposes nuclear energy and there are limits on the expansion of hydroelectric capacity, natural gas and renewables (wind, solar, biomass) constitute the most important alternatives to coal. Natural gas is currently viewed as the best option for replacing coal because of its much lower CO2 emissions and its abundance because of shale gas plays. Yet, policymakers and environmental organizations increasingly focus on renewable energy for addressing climate change.*****Determining the optimal mix of generators in a grid and the costs and benefits of integrating renewable energy is a difficult proposition. Engineers have developed a variety of energy system (grid allocation) models to examine these issues. These models are extremely useful for identifying physical obstacles and costs of integrating intermittent sources of generation into extant grids. The models fail to address (1) the incentives needed to decommission inefficient assets and invest in renewable generation, (2) jurisdictional differences across electricity grids (viz., command-and-control vs deregulated approaches), and (3) the need to calibrate models. Calibration is not an issue when every individual generator, bus and transmission intertie is modeled since engineering information is likely adequate; but it is a problem when similar generators of different ages are grouped together according to fuel type, say, as required to analyze policy in a broader context. In that case, operating costs need to be aggregated - a marginal cost function needs to be specified for generators operating in tandem as opposed to individually. Calibration methods help one discover the costs of operating multiple, often disparate generators as a single unit. Calibration facilitates the study of the costs and benefits of optimally integrating renewables into existing grids, or reconfiguring the generation mix. It is essential for policy analysis. These three issues have not previously been adequately explored in energy systems modeling. The current research intends to rectify this using Alberta-BC (wind/hydro) and Nova Scotia-Newfoundland and Labrador (tidal/hydro) as case studies.****Key Words: renewable energy; electricity grids and storage; calibrating mathematical models