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

在加拿大，38%的二氧化碳排放量来自343家大型排放者，其中14家是燃煤发电厂，占二氧化碳总排放量的12%。在全球范围内，中国、澳大利亚、印度、美国和德国45%或更多的电力来自煤炭。尽管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（尤其是电力需求）预计将增长41%。由于公众反对核能，水力发电能力的扩张也受到限制，天然气和可再生能源（风能、太阳能、生物质能）构成了煤炭最重要的替代品。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，而且页岩气储量丰富。然而，政策制定者和环保组织越来越关注可再生能源，以应对气候变化。*****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。工程师们已经开发了各种各样的能源系统（电网分配）模型来研究这些问题。这些模型对于识别将间歇性发电资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发电所需的激励措施，(2)电网之间的管辖权差异（即命令和控制与放松管制的方法），以及(3)校准模型的需要。由于工程信息可能足够，当每个单独的发电机，总线和传输互连被建模时，校准不是一个问题；但是，当不同年代的类似发电机根据燃料类型归类时，就会出现问题，比如，这是在更广泛的背景下分析政策所必需的。在这种情况下，运行成本需要汇总——需要为串联运行而不是单独运行的发电机指定边际成本函数。校准方法可以帮助人们发现将多个（通常是不同的）发电机作为一个单元运行的成本。校准有助于研究将可再生能源优化整合到现有电网或重新配置发电组合的成本和收益。这对政策分析至关重要。这三个问题以前在能源系统建模中没有得到充分的探讨。目前的研究打算用阿尔伯塔-卑诗省（风能/水力）和新斯科舍省-纽芬兰和拉布拉多（潮汐/水力）作为案例研究来纠正这一点。****关键词：可再生能源；电网和储能；校正数学模型

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