Advanced Optimization and Cost Estimation for Utilities and Interruptible Customers

针对公用事业和不间断客户的高级优化和成本估算

• 批准号: 9726577
• 负责人: Peter Luh
• 金额: $ 11.65万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2001-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9726577&HistoricalAwards=false
• 关键词: Advanced; Optimization; Cost; Estimation; Utilities

ECS-9726577 Luh Scheduling power systems and performing inter-utility power transactions are important activities to supply reliable, economic, and clean power. The economics of scheduling and transaction decisions, however, are significantly affected by uncertainties, including inaccurate forecasted demand, uncertain unit availability, and the random occurrences of bilateral power transaction opportunities. The impact of uncertainties propagates to utilities' industrial customers with interruptible load, as their prices or consumption limits may be changed forcing them to alter their resource allocation and personnel commitment. To reduce the cost of electricity through effective managing uncertainties and to provide reliable price estimates for industrial customers with interruptible load, a coalition of University of Connecticut (UConn), Northeast Utilities (NU), Taylor & Fenn, and Praxair is formed. The specific objectives of the proposed research are: 1 . To develop an effective stochastic-fuzzy optimization method for the short-term scheduling and transaction problem to minimize cost, reduce solution variance, and manage risks. 2. To further improve the above results by developing a new generation of optimization approaches based on a synergistic combination of "Lagrangian relaxation" and neural networks for better and faster resolution of larger and more complicated problems - problems covering a larger number of units, with three levels of reserve, or with a longer planning horizon. 3. To develop methods to accurately and robustly estimate the costs for providing bundled or unbundled electricity products and services, and to explore other means for a utility to better serve its industrial customers with interruptible load. The optimization of large-scale, mixed-integer uncertain systems such as the scheduling and transaction problem considered here for near-optimal cost with low variance is theoretically important and practically challenging. The foundations needed t o be laid for and the potential benefits of neural networks for such applications are intriguing and fascinating. Based on our excellent results obtained thus far, we believe that the proposed research shall not only help utilities cut costs, reduce variance, and provide better products and services for interruptible customers, but also open up a new generation of neural optimization approaches for other applications in power systems and beyond.

ECS-9726577 Luh 调度电力系统和执行电力公司间的电力交易是提供可靠、经济和清洁电力的重要活动。 然而，调度和交易决策的经济性受到不确定性的显著影响，包括不准确的预测需求，不确定的机组可用性，以及双边电力交易机会的随机发生。 不确定性的影响传播到电力公司的工业客户与可中断负荷，因为他们的价格或消费限制可能会改变迫使他们改变他们的资源分配和人员承诺。 为了通过有效管理不确定性来降低电力成本，并为具有可中断负荷的工业客户提供可靠的价格估计，康涅狄格大学（UConn），东北公用事业公司（NU），Taylor Fenn和Praxair组成了一个联盟。 拟议研究的具体目标是： 1 .针对短期作业排程与交易问题，提出一种有效的随机模糊最佳化方法，以达到成本最小化、解差异最小化及风险管理的目的。 2.为了进一步改进上述结果，开发了新一代的优化方法，其基础是“拉格朗日松弛”和神经网络的协同组合，以便更好、更快地解决更大、更复杂的问题-涉及更多单位、具有三级储备或具有更长规划期的问题。 3.制定方法，以准确和稳健地估计提供捆绑或非捆绑电力产品和服务的成本，并探索公用事业公司更好地为工业客户提供可中断负荷服务的其他方法。 大规模混合整数不确定系统的优化问题，如调度和交易问题，考虑在这里的近最优成本与低方差是理论上重要的和实际的挑战。 需要奠定的基础和神经网络对这些应用的潜在好处是有趣和迷人的。 基于我们迄今为止所取得的优秀成果，我们相信，所提出的研究不仅可以帮助电力公司降低成本，减少差异，为可中断客户提供更好的产品和服务，而且还为电力系统等领域的其他应用开辟了新一代的神经优化方法。