Input-output models used for macroeconomic impact and policy analysis are often characterised by large data sets and resource-heavy computing. However, the types of results they provide are sensitive to uncertainty in their core assumptions. Various approaches have been proposed to account for the uncertainty in one or more parts of the analysis assumptions. Although it is standard practice to include varied cases in policy analysis, the notion of stochasticity of parameters across periods of time is less widespread. Costly numerical computing and difficulty in interpreting the results complicate this approach. In this work, we adapt an environmentally-extended dynamic econometric input-output model to account for uncertainty of various kinds. We demonstrate the methodology in the case of building renovation. The results provide insight into situations where explicitly considering uncertainty as part of the analysis is useful, as well as others in which no additional information was gained from such treatment.

Industrial Clusters, especially those based on biologically sourced materials and their derivative products, can play an important role in the global shift to more sustainable production methods and ecological economic systems. The concept of cluster, however, is difficult to define and study. This paper presents quantitative methods based on Input-Output and Operations Research analysis to establish and plan cluster operations and complement that with qualitative reflections on the nature of these clusters. The purpose is to bring together both dimensions and demonstrate their complementarity, with social and policy aspects being as important considerations as techno-economic-driven ones. Using a case study, hypothetical clusters using numerical methods are created; the clusters produced by numerical methods point to and raise important issues related to the need to utilize qualitative analysis in conjunction to pure economic motives while designing/planning industrial clusters.

We investigate the potential for double or even multiple dividends arising from a climate and energy tax reform (CETR), using a regional computable general equilibrium model. Such dividends indicate if government revenues raised from energy-related environmental taxes and recycled back to households or industries through (regional) social security contributions will yield welfare gains larger than gross cost. Building on existing double dividend theory, we broaden the scope by considering both social and regional aspects of a CETR. We explore the use of household transfers and regional payroll taxes as recycling instruments and investigate to what extent wage formation on the labor market has an effect. For Norway, our results indicate that a CETR may conflict with sub-national policy goals under all assessed scenarios. In particular, this holds for income inequality. Although our analysis concerns the social, economic and environmental aims of a Norwegian policy, the approach can be generalized to, e.g., a European context.

Selecting portfolios of electricity production assets, energy sources and market participation strategies facilitates usage and management of complementary resources. It helps also power producers to address uncertainties and to balance profit contributions, costs and risks. Therefore, portfolios should be composed wisely. Our paper will bring concepts of portfolio optimization closer to private energy producers. We highlight important aspects to be considered and outline key value drivers. However, we call also for critical thinking if portfolios of physical assets should be considered a panacea to address uncertainty in power generation and market operations. An example demonstrates that, sometimes, financial instruments rather than diversification into renewables may prove more efficient to hedge risk In addition to the possibility of hedging through the portfolios, portfolio management can yield benefits for internal physical balancing and market access but the value in terms of additional profit and risk reduction depends on market conditions.

As a consequence of past decades of extensive afforestation in Norway, mature forest volumes are increasing. National forestry politics call for sustainable and efficient resource usage and for increased regional processing. Regional policies seek to provide good conditions for such industries to be competitive and to improve regional value creation. We demonstrate how methods from operations research and regional macro-economics may complement each other to support decision makers in this process. The operations research perspective is concerned with finding an optimally designed wood value chain and an aggregated planning of its operations, taking a holistic perspective on strategic-tactical level. Using Input-Output analysis methods based on statistics and survey data, regional macro-economics helps to estimate each industry actor's value creation and impact on society beyond immediate value chain activities. Combining these approaches in a common mathematical optimization model, a balance can be struck between industry/business and regional political interests. For a realistic case study from the northern part of coastal Norway, we explore this balance from several perspectives, investigating value chain profits, economic ripple effects and regional resource usage.

We discuss how an optimization model can be used together with a scenario generation procedure to provide valuable analysis for companies operating in a natural gas value chain. The solution time of the optimization model can be considerable for some model specifications, so a large scale sampling from the distribution of the uncertain parameters would lead to intractable solution times. By using a scenario generation procedure we can, however, drastically reduce the required amount of analyses necessary to run. We discuss two different procedures in this paper: moment-matching and copulas. We also demonstrate the application on a gas transportation network similar to the one on the Norwegian Continental Shelf. The data used in the analysis are synthetic, but with realistic values.

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.

National and regional authorities worldwide have passed legislation in order to mitigate climate change. For example, the “20-20-20” targets of the European Commission include a 20% improvement in energy efficiency by 2020 relative to 1990 levels (EU, 2008; EU, 2009). One pathway for this objective to be achieved is via improved operational and retrofitting practices in existing buildings. Since the building sector is responsible for nearly 40% of the energy consumed in the EU (EU, 2011), sectoral improvements could make a substantial impact overall. Contemporaneously, electricity-sector deregulation in most industrialised countries aims to improve economic efficiency by providing more transparent price signals to producers and consumers (Wilson, 2002). Indeed, unlike the hierarchical, vertically integrated paradigm, the deregulated one facilitates more decentralised decision making. On the one hand, this creates incentives for building managers to respond to market conditions by adjusting their set points in the short term (taking into account weather forecasts and occupancy levels) or by retrofitting in the long term; yet, on the other hand, they will have to guard against volatile energy prices and to trade off both investment and operational decisions over time. In effect, consumers need better decision support for potentially conflicting objectives, e.g., lowering energy costs, managing risk, and improving energy efficiency. From the perspective of public building managers in the EU, an optimisation approach based on modelling energy flows may enhance decision making. In particular, our preliminary results based on data from test sites in Austria and Spain (as part of the EU FP7 EnRiMa project) indicate how dynamic zone temperatures for heating via conventional radiators and heating/cooling via HVAC systems reduce energy consumption by 10%. This is possible by responding to external conditions and internal loads while taking into account the thermodynamics of the heating/cooling system and the building’s physics. Longer-term savings from retrofitting may also be possible and are being investigated.

We present a new model to support strategic planning by actors in the liquefied natural gas market. The model takes an integrated portfolio perspective and addresses uncertainty in future prices. Decision variables include investments and disinvestments in infrastructure and vessels, chartering of vessels, the timing of contracts, and spot market trades. The model accounts for various contract types and vessels, and it addresses losses. The underlying mathematical model is a multistage stochastic mixed-integer linear problem. Industry-motivated numerical cases are discussed as benchmarks for the potential increases in profits that can be obtained by using the model for decision support. These examples illustrate how a portfolio perspective leads to decisions different than those obtained using the traditional net present value approach. We show how explicitly considering uncertainty affects investment and contracting decisions, leading to higher profits and better utilization of capacity. In addition, model run times are competitive with current business practices of manual planning.

World Scientific Series in FinanceStochastic Programming, pp. 259-288 (2013) No AccessMulti-Stage Stochastic Programming for Natural Gas Infrastructure Design with a Production PerspectiveLars Hellemo, Kjetil Midthun, Asgeir Tomasgard, and Adrian WernerLars HellemoDepartment for Industrial Economics and Technology Management, Norwegian University of Science and Technology, Alfred Getz veg 3, NO–7491 Trondheim, Norway, Kjetil MidthunDepartment for Applied Economics and Operations Research, SINTEF Technology and Society, NO–7465 Trondheim, Norway, Asgeir TomasgardDepartment for Industrial Economics and Technology Management, Norwegian University of Science and Technology, Alfred Getz veg 3, NO–7491 Trondheim, Norwaycorresponding author, and Adrian WernerDepartment for Applied Economics and Operations Research, SINTEF Technology and Society, NO–7465 Trondheim, Norwayhttps://doi.org/10.1142/9789814407519_0010Cited by:9 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: We present a multi-stage stochastic model that analyzes investments in natural gas fields and infrastructure. New projects are evaluated together with existing infrastructure and planned expansions. Several uncertain factors both upstream and downstream such as reservoir volumes, the composition of the gas in new reservoirs, market demand and price levels can influence the optimal decisions. The model focuses also on the impact of the sequencing of field developments and new infrastructure on the expected security of supply. In order to analyze all these aspects in one model, we propose a novel approach to scenario trees, combining long-term and short-term uncertainty. Dimensionality and solution times of realistic investment cases from the Norwegian Continental Shelf are discussed. FiguresReferencesRelatedDetailsCited By 9A dual-level stochastic fleet size and mix problem for offshore wind farm maintenance operationsMagnus Stålhane, Kamilla Hamre Bolstad, Manu Joshi and Lars Magnus Hvattum21 December 2020 | INFOR: Information Systems and Operational Research, Vol. 59, No. 2A decomposition approach for optimal gas network extension with a finite set of demand scenariosJonas Schweiger and Frauke Liers17 February 2018 | Optimization and Engineering, Vol. 19, No. 2Optimization techniques for the Brazilian natural gas network planning problemSergio V. B. Bruno, Leonardo A. M. Moraes and Welington de Oliveira3 November 2015 | Energy Systems, Vol. 8, No. 1Stochastic Modeling of Natural Gas Infrastructure Development in Europe under Demand UncertaintyMarte Fodstad, Ruud Egging, Kjetil Midthun and Asgeir Tomasgard1 Sep 2016 | The Energy Journal, Vol. 37, No. 01Adding flexibility in a natural gas transportation network using interruptible transportation servicesMarte Fodstad, Kjetil T. Midthun and Asgeir Tomasgard1 Jun 2015 | European Journal of Operational Research, Vol. 243, No. 2Stochastic model for energy commercialisation of small hydro plants in the Brazilian energy marketVitor L. de Matos, Mauro A. G. Sierra, Erlon C. Finardi, Brigida U. Decker and André A. S. Milanezi29 April 2014 | Computational Management Science, Vol. 12, No. 1Optimization Model to Analyse Optimal Development of Natural Gas Fields and InfrastructureKjetil Trovik Midthun, Marte Fodstad and Lars Hellemo1 Jan 2015 | Energy Procedia, Vol. 64Multi-horizon stochastic programmingMichal Kaut, Kjetil T. Midthun, Adrian S. Werner, Asgeir Tomasgard and Lars Hellemo et al.22 August 2013 | Computational Management Science, Vol. 11, No. 1-2THE INFRASTRUCTURE IMPLICATIONS OF THE ENERGY TRANSFORMATION IN EUROPE UNTIL 2050 — LESSONS FROM THE EMF28 MODELING EXERCISEFRANZISKA HOLZ (Germany) and CHRISTIAN VON HIRSCHHAUSEN (Germany & Germany)19 December 2013 | Climate Change Economics, Vol. 04, No. supp01 Stochastic ProgrammingMetrics History PDF download

We study the application of the stochastic programming framework to the analysis of complex agency problems under exogenous and endogenous uncertainty, presenting several models that deal with different types of such uncertainty. We demonstrate that the utilization of this framework extends the possibilities for the definition of parameters of incentive schedules. In this paper, we often refer to a model in a telecommunication environment consisting of a regulator (principal) and a service provider (agent) and study different aspects of regulation and licensing. However, the results can easily be generalized to other principal agent relationships.

We present an investment analysis tool for natural gas infrastructure development. The model takes a system perspective and considers all existing infrastructure as well as the potential expansions. We formulate the investment problem as a deterministic mixed-integer linear program. We extend existing infrastructure analysis models within natural gas by adding pressure flow relationships and by modeling the gas quality in the transportation system. In this paper we present the motivation for the model functionality as well as the main components in the model. We also discuss a relevant investment case from the Norwegian Continental Shelf to exemplify typical model sizes as well as solution times. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the organizing committee of 2nd Trondheim Gas Technology Conference.

Early mobile phones only provided voice transmission, for a fee. They have now evolved into voice and online data portals for providing additional services through 3rd party vendors. These service providers (vendors) are given access to a customer base "owned" by the mobile phone companies, for a fee. Typically customers make two payments: to the mobile phone company for phone services and to the 3rd party vendors for specific services bought from them. Variations to the above business model may involve outsourcing the online portal and/or acquiring customers from other independent portals. For these scenarios, we study how the fees for phone service and customer access are established and how they may relate to the prices of vendor services, and which services should be located on the portal - all in a game-theoretic context. Our results prove that it is possible to reorganize revenue flows through an invoicing process that may benefit the mobile network operator more than the other parties. In addition, we establish optimality in terms of the number of vendors on the portal, and determine a rank-ordering of vendors for their inclusion into the portal. (C) 2011 Elsevier B.V. All rights reserved.

We consider the case when part of the uncertainty faced by a decision maker is derived from actions of another independent actor who pursues her own aims. Each party sets its decisions in the next time period in response to the other party's policy. We model this situation by introducing some ideas from game theory, but unlike this theory we do not focus on equilibrium and related optimality notions. Instead, we follow the framework of stochastic programming and take the view of one of the decision makers. Our model is placed in a telecommunication environment with a network owner and operators without their own network facilities. We give an extension to a multiperiod model.