Science and engineering involve discovery, representation, explanation, and exploitation of recurrent patterns, observed as phenomena. Model-based representations describe not only natural phenomena and engineered pro...
详细信息
Science and engineering involve discovery, representation, explanation, and exploitation of recurrent patterns, observed as phenomena. Model-based representations describe not only natural phenomena and engineered products, but also the socio-technical systems of systems that carry out scientific study, product engineering, medical practice, public health, commerce, and regulation. The term "Regulatory Science" invites us to represent and understand innovation, regulation and their intended and actual consequences as observable system phenomena in their own right, using scientific and engineering principles, tools, and insights. This article summarizes three classes of model-based reference patterns central to representing, understanding, communicating, and enhancing systems of innovation, regulation, and improvement over life cycles. In order of increasing scale, these pattern classes are (1) the domain-independent pattern of model-based representation of system phenomena (the S*Metamodel) in the sciences and engineering disciplines, underlying all modeling and simulation;(2) domain-specific patterns representing families of natural systems and engineered products in their life cycle contexts;and (3) the large-scale Innovation Ecosystem Pattern, in which science, engineering, commerce, medicine, and regulation are performed, planned, and advanced-including sharing of managed models and data across ecosystems. All three are applied by the Model-Based patterns Working Group of the International Council on systems Engineering (INCOSE).
Short-term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates mar...
详细信息
Short-term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns-combinations of status flags for generating units and transmission lines. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm.
Short-term prediction of system variables with respect to load levels is highly important for market operations and demand response programs in wholesale power markets with congestion managed by locational marginal pr...
详细信息
ISBN:
(纸本)9781424483570
Short-term prediction of system variables with respect to load levels is highly important for market operations and demand response programs in wholesale power markets with congestion managed by locational marginal prices (LMPs). Previous studies have conducted local sensitivity analyses for LMPs at specific system operating points. This study undertakes a more global analysis of system variable sensitivities when LMPs are derived from DC optimal power flow solutions for day-ahead energy markets. The possible system states are first partitioned into subsets ("system patterns") based on relatively slow-changing attributes. It is next established analytically that there is a fixed linear-affine mapping between bus load patterns and corresponding system variables, conditional on a particular system pattern. It is then explained how this global piecewise linear-affine mapping can be used to predict system patterns corresponding to forecasted load patterns, hence also dispatch levels, LMPs, and line flows. A 5-bus case study is used to illustrate the accuracy of the proposed prediction method.
暂无评论