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The Integrated Energy Decision Support System
Abstract
In this paper we introduce a decision support system framework termed the Integrated Energy Decision Support System (IEDSS). IEDSS was developed for energy planning at national and regional levels to inform energy planners at multiple levels of government. IEDSS employs system dynamics modeling to enable the rapid evaluation of the outcomes of different supply and demand policies at the national level. Agent-Based models are used to mimic the interactions between different entities when applying the framework at the regional level of government. Within the IEDSS framework policy makers specify a set of policy decisions and choose from a set of uncertain futures to investigate the performance of their policy decisions. Together these form a scenario for which IEDSS computes a set of output parameters that are used to evaluate the resulting outcome. As a model-driven DSS, IEDSS can be utilized in two ways. The first is as a single-user DSS that deploys a scenario-based planning approach which informs decision makers by mapping the solution space and the resultant effects caused by their policy choices. The second is as a group-based DSS that enhances communication and collaborative decision making between multiple entities. IEDSS is developed on a software platform that utilizes the front-end computation to handle templates, style sheets, and visualizations, while the backend is focused on data retrieval, models execution, and performance optimization. IEDSS was developed to address the power sector of the Kingdom of Saudi Arabia as a case study, but the framework and capabilities of its platform are applicable to any generalized case.
... An example in the literature: Swinerd and McNaught [36] do not mention this class explicitly. However, the model they provide in their other works [46,53], reveal that they acknowledge the existence of this class which combines Classes B and C. The only other instance of this class in our review is Alfaris and others [54] that present a model for national energy planning in Saudi Arabia. • An example in PWM: The AB module of injection wells explained in the Class B example is connected to a SD module of oil production in a feedback loop. ...
This paper explores the possibility and plausibility of developing a hybrid simulation method combining agent-based (AB) and system dynamics (SD) modeling to address the case study of produced water management (PWM). In southeastern New Mexico, the oil and gas industry generates large volumes of produced water, while at the same time, freshwater resources are scarce. Single-method models are unable to capture the dynamic impacts of PWM on the water budget at both the local and regional levels, hence the need for a more complex hybrid approach. We used the literature, information characterizing produced water in New Mexico, and our preliminary interviews with subject matter experts to develop this framework. We then conducted a systematic literature review to summarize state-of-the-art of hybrid modeling methodologies and techniques. Our research revealed that there is a small but growing volume of hybrid modeling research that could provide some foundational support for modelers interested in hybrid modeling approaches for complex natural resource management issues. We categorized these efforts into four classes based on their approaches to hybrid modeling. It appears that, among these classes, PWM requires the most sophisticated approach, indicating that PWM modelers will need to face serious challenges and break new ground in this realm.
... As carbon emissions and cost become evenly weighted the model turns to gas generation to produce relatively environmentally friendly power. This is in keeping with previous studies of the Kingdom's generation expansion like the work by Gowharji, et al. (2014) and Alfaris, Khiyami and Alawad (2015). It is especially interesting to note that gas investments are preferred to PV in the 50-50 scenario. ...
Increased interest in renewable generation has made siting constraints more prominent in power system planning. In addition the power system is increasingly being considered as one of many coupled critical infrastructure systems in nexus’ such as the food-energy-water nexus and the gas-electricity nexus. This paper describes a power system planning approach within a graph theoretic framework that allows for the handling of the geographical aspects of electricity production and distribution as well as the interaction of electricity network with other critical infrastructures.
International and national policies stress the importance of spatial planning for the long-term sustain- ability of regions. This paper identifies the extent to which the spatial planning in a Swedish region can be characterised as a collaborative learning process. By combining qualitative interviews and systems thinking methods we analysed the main attributes of public-led spatial (i.e. comprehensive) planning in nine municipalities representing a steep urban–rural gradient in the Bergslagen region of Central Sweden. We show that the attributes of strategic spatial planning needed for collaborative learning were absent or undeveloped. All studied municipalities experienced challenges in coordinating complex issues regarding long-term planning to steer territorial development and help to solve conflicts among competing inter- ests. Stakeholder participation was identified as a basic condition for social learning in planning. Together with stakeholders we identified the causal structure behind stakeholder participation in municipal plan- ning processes, including main drivers and feedback loops. We conclude that there is a need for arenas allowing and promoting stakeholder activity, participation and inclusion that combines both bottom-up and top-down approaches, and where evidence-based collaborative learning can occur.
Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sens-ing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better under-standing and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recom-mendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.
In critical energy infrastructure sectors (e.g. electric power generation, natural gas transporta- tion, oil refining capacity) maintaining certain level of excess capacity is socially valuable (as it serves to protect against unexpected market conditions) but not necessarily compatible with the incentives for individual firms in the market. In this paper, we develop a dynamic oligopoly model with a stochastically growing demand to analyze the inherent tension in market-based incentives for capacity expansion where capacity additions take place over long time lags. Our results indicate that the market fails to induce the socially optimal level of capacity. However, the magnitude of this failure varies greatly as a function of entry costs and the relative profitability of investments in the market (as measured by the ratio of maximum markup over production costs and investment costs). In general, the likelihood of insufficient capacity in equilibrium increases with decreas- ing probability of demand growth, increasing discount and depreciation rates and/or increasing investment and/or production costs. We discuss the public policy implications of our results.
In recent years, the interest in modeling complex engineering systems has grown rapidly. Due to their highly nonlinear nature coupled with their critical importance, the task of modeling such systems becomes a high-risk, high-payoff endeavor. There are many complexities associated with understanding and modeling the dynamics of such systems, these include functional, spatial and temporal complexities. The main objective of this paper is to present a modeling framework that enables the planning and evaluation of complex engineering systems taking into account these multidimensional complexities. The framework uses a multidimensional hierarchical decomposition of complex engineering systems combined with modeling techniques such as System Dynamics and Agent Based Modeling. Decomposition and modeling are integrated to simulate possible future states of the system and to evaluate different plans and scenarios for system evolution using key performance indicators (KPIs). The paper also presents a case study demonstrating the use of the proposed framework for an integrated water model applied to the kingdom of Saudi Arabia.
We have an addiction to fossil fuels, and it’s not sustainable. The developed
world gets 80% of its energy from fossil fuels; Britain, 90%. And
this is unsustainable for three reasons. First, easily-accessible fossil fuels
will at some point run out, so we’ll eventually have to get our energy
from someplace else. Second, burning fossil fuels is having a measurable
and very-probably dangerous effect on the climate. Avoiding dangerous
climate change motivates an immediate change from our current use of
fossil fuels. Third, even if we don’t care about climate change, a drastic
reduction in Britain’s fossil fuel consumption would seem a wise move if
we care about security of supply: continued rapid use of the North Sea Photo by Terry Cavner.
oil and gas reserves will otherwise soon force fossil-addicted Britain to depend
on imports from untrustworthy foreigners. (I hope you can hear my
tongue in my cheek.)
How can we get off our fossil fuel addiction?
There’s no shortage of advice on how to “make a difference,” but the
public is confused, uncertain whether these schemes are fixes or figleaves.
People are rightly suspicious when companies tell us that buying their
“green” product means we’ve “done our bit.” They are equally uneasy
about national energy strategy. Are “decentralization” and “combined
heat and power,” green enough, for example? The government would have
us think so. But would these technologies really discharge Britain’s duties
regarding climate change? Are windfarms “merely a gesture to prove our
leaders’ environmental credentials”? Is nuclear power essential?
We need a plan that adds up. The good news is that such plans can be
made. The bad news is that implementing them will not be easy.
Complex decision making is an important aspect in planning large man-made systems. These systems tend to be of high complexity, which requires an automated, intelligent approach to realize all dimensions of the problem. Decision Support Systems (DSS) are one approach to automate this process by providing modeling and analytical capabilities that assist users in making decisions. In this paper, we present the architecture of a web-based DSS platform for multidimensional modeling (including functional, spatial, and temporal dimensions) of complex engineering systems. We describe the breakdown of the modeling process of such systems in a multidimensional hierarchical manner. We present a case study for power planning in Saudi Arabia.
In 1965, a time when quantitative, computer-driven planning was very much in vogue, Royal Dutch Shell started experimenting with a different way of looking into the future: scenario planning. Shell’s practice has now survived for almost half a century and has had a huge influence on how businesses, governments, and other organizations think about and plan for the future. The authors interviewed almost every living veteran of the Shell scenario planning operation, along with top Shell executives through the years. They identify several principles that both define the process at Shell and help explain how it has survived and thrived for so long.
For instance, Shell scenarios are stories, not predictions, and are designed to help break the habit, ingrained in most corporate planning, of assuming that the future will look much like the present. They must above all be plausible, with a logical story line, in order to encourage intuition and judgment. They create a safe space for dialogue and for acknowledging uncertainty.
They must also be relevant, not simply disruptive and challenging. And they need some quantification to be credible—but the numbers must flow from the stories, rather than the other way around. Otherwise, there’s always the danger that quantitative models will hide assumptions and constrain thinking rather than refine it.
Because scenarios follow a rhythm distinct from the annual strategy cycle, they allow an organization to see realities that would otherwise be overlooked.
In order to be mindful of the overarching theme of reducing global dependency on oil for energy production, careful execution of current thermal and gas fired conventional capacity must be taken into consideration. Given the high energy demand profiles experienced in some developing countries, heavy investments in renewable and alternative generation may not be enough to suffice this ever growing demand. This is especially the case when dealing with high seasonality demand. This paper develops a modeling framework by which policy makers can examine a wide range of possible solutions to this problem through a multi-attribute scenario analysis approach developed. Through rapid prototyping of multiple curated what if scenarios, energy planners can analyze a wide range of complimentary conventional capacity portfolios that work hand in hand with the existing renewable and alternative efforts to achieve an overall desired energy outlook. The framework was developed and applied with the Kingdom of Saudi Arabia as a case study. The scenarios analyzed herein span various demand growths, nuclear and renewable deployment plans, as well as various conventional capacity expansion options. Finally a selected set of scenarios are evaluated across multiple trade-off spaces and discussed.
A prototype energy signal tool is demonstrated for operational whole-building and system-level energy use evaluation. The purpose of the tool is to give a summary of building energy use which allows a building operator to quickly distinguish normal and abnormal energy use. Toward that end, energy use status is displayed as a traffic light, which is a visual metaphor for energy use which is substantially different from expected (red and yellow lights) or more or less the same as expected (green light). Which light to display for a given energy end-use is determined by comparing expected energy use to actual energy use. As expected energy use is necessarily uncertain, we cannot choose the appropriate light with certainty. Instead the energy signal tool chooses the light by minimizing the expected cost of displaying the wrong light. The expected energy use is represented by a probability distribution. Energy use is modeled by a low-order lumped parameter model. Uncertainty in energy use is quantified by a Monte Carlo exploration of the influence of model parameters on energy use. Distributions over model parameters are updated over time via Bayes’ theorem. The simulation study is devised to assess whole building energy signal accuracy in the presence of uncertainty and faults at the submetered level, which may lead to tradeoffs at the whole building level not detectable without submetering.
This is the electronic version of the book which is also available in hardback and paperback.
In this paper we present the Collaborative Planning Interface (CoPI), a web-based multiuser collaboration interface platform for planning of complex systems. The interface provides analytical and visualization components to support decision makers. The Interface is designed using a user-centered design approach, while considering existing tools and environments in the field of decision support systems. The architecture and structure of the Interface are described as well as the flow of the user experience within the system. Finally, a case study explains the use of CoPI in collaborative policy planning for large-scale infrastructures.
Critical infrastructure analysis often involves overwhelming volumes of complex, heterogeneous, interdependent information. Human judgment is essential to the analysis as insights and understandings are synthesized from information that is often complex, dynamic, incomplete, diverse, conflicting and even deceptive. Yet, our ability to collect information is increasing at rates far beyond our ability to analyze it. Visual analytics–the science of analytical reasoning facilitated by interactive visual interfaces–can help analysts obtain better insights and understanding with greater efficiency. This paper discusses the research challenges involved in applying interactive visualization to critical infrastructure analysis. The research challenges are organized around three dimensions that are adapted from metrics proposed by Scholtz (2006) [8] for evaluating human information interaction systems. The challenges are illustrated using examples from the integrated modeling and simulation of critical infrastructures.
This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled. Commonly understood examples are used throughout the paper; all models are specified in the visual language supported by AnyLogicTM tool. We view and present Agent Based modeling not as a substitution to older modeling paradigms but as a useful add-on that can be efficiently combined with System Dynamics and Discrete Event modeling. Several multi-paradigm model architectures are suggested.
Making sense of complex systems is difficult due in part to the open, nonlinear, multi-dimensional, highly interdependent
nature of such systems. For such systems, modeling and simulation may be the only viable means for facilitating the sensemaking
process. In this chapter, we explore a particular form of modeling and simulation, called integrated modeling and simulation,
which is well-suited to empower users to make sense of complex systems. We demonstrate the potential of integrated modeling
and simulation through the Integrated Modeling Environment (IME).We also illustrate how the design of the IME respects important
implications of sensemaking on integrated modeling and simulation.
In this study, an integrated scenario-based multi-criteria decision support system (SMC-DSS) is developed for planning water resources management in the Haihe River Basin. The SMC-DSS incorporates techniques of scenario analysis, multiobjective programming (MOP), and multi-criteria decision analysis (MCDA) within a decision support systems (DSS) framework. The system components include system composition, scenario generation, scenario quantification, multiobjective optimization, and scenario evaluation. SMC-DSS can solve complex water resources management planning problem which may involve several conflict objectives, and can deal with uncertainties expressed as possible outcomes. The SMC-DSS can be used for assessing management policy impacts on socio-economic and environmental sectors and supporting the formulation of relevant management policies under uncertainty. It is effective for large-scale problems with thousands of parameters and variables and in association with uncertain features. The developed SMC-DSS is then applied to planning water resources management in the Haihe River Basin, China. A number of scenarios based on different water resources management policies are conducted. The results indicate that different water management policies could lead to varied patterns for regional economic development and environmental protection. The results obtained are valuable for identifying desired water resources management policies and planning sustainable regional development patterns.
Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi-faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation-wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domainoriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in-depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.
The increasing importance and complexity of land and natural resource management are creating a need for ecosystem-based management (EBM). Multiple criteria decision analysis (MCDA) combined with geographic information systems (GIS) can integrate factors related to the triple bottom line of ecological, economic, and social perspectives required by EBM. However, GIS-based MCDA is limited in this role because (i) it rarely integrates or encourages an exploration phase in preparation for structured evaluation and (ii) inexperienced users may find MCDA methods and GIS software difficult to use. This paper presents a novel approach for (i) supporting an exploration phase to help structure a problem and (ii) integrating the exploration and evaluation phases in an easy-to-use software system. The approach was validated through a land-management case study in a forest-dominated landscape with a variety of stakeholders. Case-study participants used the approach to rate areas within a timber harvest plan based on their potential for conflict with conservation values. The case-study decision analysis determined that between 1.3% and 6.6% of the harvest plan area had a conservation rating of 0.30 or higher on a scale of 0-1. The system was made available to the forest industry and other stakeholders to support harvest plan adjustments, demonstrating how such tools can be used to improve and integrate our knowledge of forest ecology and management. Assessment of participant feedback reveals that an exploration phase is effective in helping understand a problem and prepare for multiple criteria evaluation (MCE). Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.
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