Robust discrimination between uncertain management alternatives by iterative reflection on crossover point scenarios: Principles,design and implementations

When comparing environmental management alternatives, there is a need to assess the effect of uncertainty in the underlying model(s) and future conditions on robustness of recommendations. At times, it may be difficult or undesirable to specify the uncertainty in inputs and parameters a priori. An alternative approach instead generates crossover points, describing scenarios where the preferred alternative will change (i.e. alternatives are of equal value), and prompts the analyst to assess their plausibility a posteriori. This paper extends previous work by introducing principles, design and implementation of a new method to analyse crossover points. It reduces the complexity of dealing with many variables by identifying single crossover points of greatest concern, and progressively building understanding through three stages of analysis. We present three implementations using R, Excel and a web interface. They use two examples involving cost-benefit analysis of managed aquifer recharge and the water footprint impact of changing diets.     

Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty

A variety of model-based approaches for supporting decision-making under deep uncertainty have been suggested, but they are rarely compared and contrasted. In this paper, we compare Robust Decision-Making with Dynamic Adaptive Policy Pathways. We apply both to a hypothetical case inspired by a river reach in the Rhine Delta of the Netherlands, and compare them with respect to the required tooling, the resulting decision relevant insights, and the resulting plans. The results indicate that the two approaches are complementary. Robust Decision-Making offers insights into conditions under which problems occur, and makes trade-offs transparent. The Dynamic Adaptive Policy Pathways approach emphasizes dynamic adaptation over time, and thus offers a natural way for handling the vulnerabilities identified through Robust Decision-Making. The application also makes clear that the analytical process of Robust Decision-Making is path-dependent and open ended: an analyst has to make many choices, for which Robust Decision-Making offers no direct guidance.     

基于CPNs场景模型的测试用例生成技术研究

将用例场景引入面向对象的软件测试中,有利于测试设计者设计测试用例。将着色Petri网(Colored Petri Nets,CPNs)模型直观可靠的特点和较好的耦合性运用到UML用例场景的描述中,进一步给出了CPNs场景模型的集成算法,并运用变色标记捕获UML规约中的场景及更多新增的场景。同时探讨了基于CPNs场景模型的测试用例生成方法。     

Urban residential heating in hot summer and cold winter zones of China—Status,modeling, and scenarios to 2030

Space heating energy consumption in urban residential areas of the hot summer and cold winter zones (HSCW) in China has increased dramatically during the last decade. Large-scale surveys and in-depth measurements of residential heating consumption is important to monitor trends. More importantly, a macro energy model that reflects the current status and that can be used to analyze future scenarios is needed. In this paper, a bottom-up model was established, and the results show the total energy consumption in the HSCW and the influences of building envelops, heating equipment efficiency, and especially occupant behavior on energy use. Related technical and policy suggestions were analyzed in different scenarios. Presently, energy consumption for heating in urban residential areas of the HSCW is relatively low, but is increasing rapidly, so the establishment of superior national building energy efficient designs requires attention. The findings suggest that the district heating network would not be feasible for the HSCW and decentralized heating should be the major heating method employed. Besides building efficiency, appliance efficiency should also be improved. Lastly, residents' behaviors in regards to the opening of windows can significantly influence energy use and should be carefully considered by policy makers and engineers.     

Energy sovereignty in Italian inner areas: Off-grid renewable solutions for isolated systems and rural buildings

This paper deals with a compact system developed to assure energy independence to buildings located in inner rural areas: the Off-Grid Box. This system features a combination of techniques that assure the use of several renewable energy sources, their storage and the rationalization of consumption.This research studies the base model of the Off-Grid Box, which entails the presence of photovoltaic panels as the energy capture system.The aim of this research is to evaluate the opportunity of realizing a system to ensure energy autonomy of rural residential buildings. To achieve energy independence, for an isolated dwelling, this paper assesses the optimal storage systems that may be combined with a photovoltaic system. To this end, an Off-Grid Box has been installed in a residential unit in central Italy. Starting from the real case, two alternative energy storage scenarios were constructed. The results can be applied to a variety of geographical settings and prove the feasibility and strategic importance of total off-grid systems for individual residential units, when they are designed in integrated terms in the area to implement small-scale-smart-grids. In rural areas, these grids should also cater for small farming businesses that feature a different consumption distribution compared to dwellings.     

Combining technology roadmap and system dynamics simulation to support scenario-planning: A case of car-sharing service

Due to the volatile market environment, the use of scenario approach comes to the forefront in business strategy. As a means of scenario planning, several approaches have been proposed and conducted. However, previous research, mainly having resorted to the expert judgment for planning and evaluation, still remains conceptual and lacks a systematic link to the planning process. In response, this paper provides an integrative approach to the technology roadmap and system dynamics to support scenario planning. The proposed approach consists of three parts: scenario building, technology roadmapping, and system dynamics simulation. The first step is to construct the scenarios which are used as inputs for the scenario planning. Second, technology roadmap is developed, incorporating the scenarios built in the first step. The technology roadmap works as a strategic framework to realize the hypothetical scenarios, linking the external and hypothetical business and internal strategies. Finally, the strategic model for technology roadmap is transferred to the operational viewpoint using system dynamics. When the simulation ends, the result of each scenario is reflected to the technology roadmapping, making the multi-path technology roadmapping. As an illustrative example, three scenarios of car-sharing business are developed and analyzed.     

太阳风暴剧情生成系统的研究

剧情是太阳风暴应急演练的驱动,控制演练的过程和发展.针对传统演练中手动设置剧情、演练剧情已知,且不能反映太阳风暴动态演化过程等问题,利用二叉树重构法建立太阳风暴剧情推理引擎,根据不同演练需求智能化地生成太阳风暴剧情方案;基于历史事件库,利用分段线性插值等方法,真实表现了难以用物理模型描述的太阳风暴动态演化过程;基于以上两项关键技术,实现了太阳风暴剧情生成系统.实验结果表明,系统生成的太阳风暴剧情,贴近实战,针对性强,可有效提升演练中各部门对突发事件的快速反应和应急处置能力.     

Scenario modelling to support industry strategic planning and decision making

The Pastoral Properties Futures Simulator (PPFS) is a dynamic systems model, developed within a participatory action research partnership with the pastoral industry of Australia's Northern Territory. The model was purpose-built to support the industry's strategic planning capacity in the face of environmental, market and institutional uncertainty. The mediated modelling process sought to maximise social learning of industry stakeholders. Simulations were conducted using scenarios representing combinations of climatic, market, institutional and technological assumptions. Stochastic parameters included rainfall and product prices. Economic and environmental performance of model farms, including greenhouse gas emissions, were estimated. A critical evaluation of the tool finds the PPFS fit for purpose. However, limitations include lack of output validation, small number of scenarios and simplistic treatment of environmental impact dimensions. With further development, the PPFS can provide a platform (a) to assist with industry planning across the whole of Northern Australia and beyond, and (b) for policy analysis and development in the context of the Australian pastoral industry.     

Designing dependable and sustainable Smart Grids – How to apply Algorithm Engineering to distributed control in power systems

In this work, we present the Smart Grid Algorithm Engineering (SGAE) process model for application-oriented research and development in information and communication technology (ICT) for power systems. The SGAE process model is motivated by the main objective of contributing application-oriented research results for distributed control concepts on a sound methodological background. With this process model, we strive for an engineering aspiration within the domain of Smart Grids. The process model is set up with an initial conceptualisation phase followed by an iterable cycle of five phases with both analytical and experimental parts, giving detailed information on inputs and results for each phase and identifying the needed actors for each phase. Simulation of large-scale Smart Grid scenarios is a core component of SGAE. We therefore elaborate on tooling and techniques needed in that context and illustrate the whole process model using an application example from a finished research and development project.     

The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations

Many practical applications in control require that constraints on the inputs and states of the system are respected, while some performance criterion is optimized. In the presence of model uncertainties or disturbances, it is often sufficient to satisfy the state constraints for at least a prescribed share of the time, such as in building climate control or load mitigation for wind turbines. For such systems, this paper presents a new method of Scenario-Based Model Predictive Control (SCMPC). The basic idea is to optimize the control inputs over a finite horizon, subject to robust constraint satisfaction under a finite number of random scenarios of the uncertainty and/or disturbances. Previous SCMPC approaches have suffered from a substantial gap between the rate of constraint violations specified in the optimal control problem and that actually observed in closed-loop operation of the controlled system. This paper identifies the two theoretical explanations for this gap. First, accounting for the special structure of the optimal control problem leads to a substantial reduction of the problem dimension. Second, the probabilistic constraints have to be interpreted as average-in-time, rather than pointwise-in-time. Based on these insights, a novel SCMPC method can be devised for general linear systems with additive and multiplicative disturbances, for which the number of scenarios is significantly reduced. The presented method retains the essential advantages of the general SCMPC approach, namely a low computational complexity and the ability to handle arbitrary probability distributions. Moreover, the computational complexity can be adjusted by a sample-and-remove strategy.