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

在加拿大，38%的二氧化碳排放来自343个大型排放国，其中14个是燃煤发电厂，占总二氧化碳排放量的12%。在全球范围内，中国、澳大利亚、印度、美国和德国45%或更多的发电量来自煤炭。虽然煤炭在一次能源中的份额预计将从2012年的30%下降到2035年的27%，但煤炭需求将不可避免地增加，因为同期整体能源需求(特别是电力需求)预计将增长41%。由于公众反对核能，水力发电能力的扩大也受到限制，天然气和可再生能源(风能、太阳能、生物质能)构成了煤炭的最重要替代品。天然气目前被视为取代煤炭的最佳选择，因为它的二氧化碳排放量要低得多，而且页岩气资源丰富。然而，政策制定者和环境组织越来越关注可再生能源来应对气候变化。*确定电网中发电机的最佳组合以及整合可再生能源的成本和收益是一个困难的命题。工程师们开发了各种能源系统(电网分配)模型来研究这些问题。这些模型对于确定将间歇性发电源整合到现有电网中的物理障碍和成本非常有用。这些模型未能解决(1)淘汰低效资产和投资可再生能源所需的激励措施，(2)电网之间的管辖权差异(即，指挥和控制方法与放松管制的方法)，以及(3)校准模型的必要性。当对每个单独的发电机、母线和传输接口进行建模时，校准不是问题，因为工程信息可能是足够的；但当不同年限的类似发电机根据燃料类型分组时，例如在更广泛的背景下分析政策所需的情况下，校准就不是问题。在这种情况下，运营成本需要汇总--需要为串联运行的发电机指定边际成本函数，而不是单独运行。校准方法帮助人们发现将多台通常是完全不同的发电机作为一个单元运行的成本。校准有助于研究以最佳方式将可再生能源整合到现有电网或重新配置发电组合的成本和收益。这对于政策分析是必不可少的。这三个问题以前在能源系统建模中没有得到充分的探讨。目前的研究打算以阿尔伯塔-BC(风能/水电)和新斯科舍省-纽芬兰和拉布拉多(潮汐/水电)为案例研究来纠正这一问题。关键词：可再生能源；电网和储存；校准数学模型

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