We study a hydropower producer’s potential for value-creation from multi-market trading given the price variations in the markets and the flexibility provided through access to hydro reservoirs. We use a perfect foresight optimization model for a price-taking hydropower producer co-optimizing his trades in the day-ahead, intra-day and balancing markets. The model is used on real market data from Norway, Sweden and Germany. The study shows a theoretical potential for added value when selling energy in multiple markets relative to optimal day-ahead sale. Most of this value is achievable also when the perfect foresight is limited to the period from day-ahead bidding until operation. Flexible production plants achieve the largest relative added values for multi-market sales, and has the largest benefit from a long horizon with perfect foresight.

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.

We present a literature survey and research gap analysis of mathematical and statistical methods used in the context of optimizing bids in electricity markets. Particularly, we are interested in methods for hydropower producers that participate in multiple, sequential markets for short-term delivery of physical power. As most of the literature focus on day-ahead bidding and thermal energy producers, there are important research gaps for hydropower, which require specialized methods due to the fact that electricity may be stored as water in reservoirs. Our opinion is that multi-market participation, although reportedly having a limited profit potential, can provide gains in flexibility and system stability for hydro producers. We argue that managing uncertainty is of key importance for making good decision support tools for the multi-market bidding problem. Considering uncertainty calls for some form of stochastic programming, and we define a modelling process that consists of three interconnected tasks; mathematical modelling, electricity price forecasting and scenario generation. We survey research investigating these tasks and point out areas that are not covered by existing literature.

We present a case study for a price-taking hydropower producer trading in the three short-term energy markets, day-ahead, intra-day and the balancing market. The study uses a scheduling software with a detailed representation of a real Norwegian power plant optimizing the operations and trade for historical market data from 2015. The motivation for the study is to make an assessment of the value of trading in multiple markets relative to day-ahead trading only, utilizing a simplifying perfect foresight assumption. This gives the basis for assessing the value of developing more complex models with a more precise representation of uncertainty in the decision process. The analysis show a significant added value from participating in more markets than day-ahead, with the balancing market giving the largest contribution.

When a petroleum well no longer serves its purpose, the operator is required to plug and abandon (P&A) the well to avoid contamination of reservoir fluids. An increasing number of offshore wells needs to be P&A’d in the near future, and the costs of these operations are substantial. Research on planning methods in order to allocate vessels that are required to perform these operations in a cost-efficient manner is therefore essential. We use an optimization approach and propose a mixed integer linear programming model based on a variant of the uncapacitated vehicle routing problem that includes precedence and non-concurrence constraints to plan a plugging campaign. P&A costs are minimized by creating optimal routes for a set of vessels, such that all operations that are needed to P&A a set of development wells are executed. In a case study, we show that our proposed optimization approach may lead to significant cost savings compared to traditional planning methods and is well suited for P&A planning purposes on a tactical level.

Summary An estimated 3,000 oil wells need to be plugged and abandoned on the Norwegian Continental Shelf (NCS), with approximately 150 new wells being drilled each year. The petroleum industry estimates the total plugging costs to be almost 900 billion Norwegian kroner (NOK), and that the work will take up to 40 years to complete. Because of the current tax regulations in Norway, the state indirectly pays 78% of the costs (approximately 700 billion NOK). It is therefore vital to reduce expenses by targeted research and development (R&D) of new technology, and to ensure better planning of plug-and-abandonment (P&A) operations in and between licenses. The current study aims to gather available data relevant for Norwegian P&A operations in an open-source database, and to develop and use a P&A planning software that serves as a decision-support tool for various problems operators face. The software can be used to generate planning schedules, identify bottlenecks in P&A operations, and analyze potential efficiency gains from technology improvements and cooperative plugging campaigns. We will use a cross-disciplinary approach that combines the fields of operations research with technological expertise. In this paper, we present the outline of the database and the current status of available data for P&A operations on the NCS, as well as a short literature review. Available data are categorized according to the different choices that need to be made within a single P&A operation, with regard to both technological aspects and the existing regulatory framework. We also discuss the type of analysis the P&A planning software is envisioned to perform. Industry, government, and ordinary tax payers will all benefit from knowledge sharing, optimized planning, and more targeted R&D efforts on this topic. The results from this study will be used to identify important cost drivers and to draw up a roadmap for future P&A-related R&D.

We consider an electricity market with two sequential market clearings, for instance representing a day-ahead and a real-time market. When the first market is cleared, there is uncertainty with respect to generation and/or load, while this uncertainty is resolved when the second market is cleared. We compare the outcomes of a stochastic market clearing model, i.e. a market clearing model taking into account both markets and the uncertainty, to a myopic market model where the first market is cleared based only on given bids, and not taking into account neither the uncertainty nor the bids in the second market. While the stochastic market clearing gives a solution with a higher total social welfare, it poses several challenges for market design. The stochastic dispatch may lead to a dispatch where the prices deviate from the bid curves in the first market. This can lead to incentives for selfscheduling, require producers to produce above marginal cost and consumers to pay above their marginal value in the first market. Our analysis show that the wind producer has an incentive to deviate from the system optimal plan in both the myopic and stochastic model, and this incentive is particularly strong under the myopic model. We also discuss how the total social welfare of the market outcome under stochastic market clearing depends on the quality of the information that the system operator will base the market clearing on. In particular, we show that the wind producer has an incentive to misreport the probability distribution for wind.

We present an analysis of the optimal development of natural gas infrastructure in Europe based on the scenario studies of Holz and von Hirschhausen (2013). We use a stochastic mixed integer quadratic model to analyze the impact of uncertainty about future natural gas consumption in Europe on optimal investments in pipelines. Our data is based on results from the PRIMES model of natural gas demand and technology scenarios discussed in Knopf et al. (2013). We present a comparison between the results from the stochastic model and the expected value model, as well as an analysis of the individual scenarios. We also performed sensitivity analyses on the probabilities of the future scenarios. Comparison of the results from the stochastic model to those of a deterministic expected value model reveals a negligible Value of the Stochastic Solution. We do, however, find structurally different infrastructure solutions in the stochastic and the deterministic models. Regarding infrastructure expansions, we find that 1) the largest pipeline investments will be towards Asia, 2) there is a trend towards a larger gas supply from Africa to Europe, and 3) within Europe, eastward connections will be strengthened. Our main finding using the stochastic approach is that there is limited option value in delaying investments in natural gas infrastructure, until more information is available regarding policy and technology in 2020, due to the low costs of overcapacity.

We consider an electricity market organized with two settlements: one for a pre-delivery (day-ahead) market and one for real time, where uncertainty regarding production from non-dispatchable energy sources as well as variable load is resolved in the latter stage. We formulate two models to study the efficiency of this market design. In the myopic model, the day-ahead market is cleared independently of the real-time market, while in the integrated stochastic dispatch model the possible outcomes of the real-time market clearing are considered when the day-ahead market is cleared. We focus on how changes in the design of the electricity market influence the efficiency of the dispatch, measured by expected total cost or social welfare. In particular, we examine how relaxing network flow constraints and, for the stochastic dispatch model, even the balancing constraints in the day-ahead part of the dispatch models affects the overall efficiency of the system. This allows the dispatch to be infeasible day-ahead, while these infeasibilities will be handled in the real-time market. For the stochastic dispatch model we find that relaxing the network flows and balancing constraints in the dayahead part of the market provides additional flexibility that can be valuable to the system. In our examples with high up-regulation cost we find a value of "overbooking" that lead to lower total costs. In the myopic model the results are more ambiguous, however, leaving too many constraints to be resolved in the real-time market only, can lead to infeasibilities or high regulation cost.

Abstract An estimated 3000 oil wells need to be plugged and abandoned on the Norwegian Continental Shelf (NCS), with approximately 150 new wells being drilled each year. The petroleum industry estimates the total plugging costs to be almost 900 billion Norwegian kroner, and that the work will take up to 40 years to complete. Due to the current tax regulations in Norway, the state indirectly pays 78% of the costs (about 700 billion Norwegian kroner). It is therefore vital to reduce expenses by targeted research and development (R&D) of new technology, and to ensure better planning of plug and abandonment (P&A) operations in and between licenses. The current study aims to gather available data relevant for Norwegian P&A operations in an open source database – and to develop and use a P&A planning software to derive plugging costs in various scenarios. The software may be used to identify bottlenecks in P&A operations and analyze potential efficiency gains from technology improvements both for single operations and on a system level. We will use a cross-disciplinary approach that combines the fields of operations research with technological expertise. In this paper, we present the outline of the database and the current status of available data for P&A operations on the NCS, as well as a short literature review. Available data is categorized according to the different choices that need to be made within a single P&A operation, both with regards to technological aspects and existing regulatory framework. We also show how this database will be used to develop a first version of the P&A planning software. Industry, government and ordinary tax payers will all benefit from knowledge sharing, optimized planning, and more targeted R&D efforts on this topic. The results from this study will be used to identify important cost drivers and to draw up a roadmap for future P&A-related R&D.

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.

We present an optimization model for analysis of system development for natural gas fields, processing and transport infrastructure. In this paper we present our experience from performing analyses for the natural gas industry with the optimization model. We also present a model extension in the form of continuous investment decisions. This extension allows the capacity in pipelines, processing facilities and compressors to be determined within a given range by the model. We also give a partial model description along with a case example that demonstrates the importance of using continuous investment decisions when considering design in natural gas systems.

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.

The fishing industry is one of the main contributors to the national economy, value creation, and employment in Norway. Furthermore, it is a significant source of export incomes. The fishing industry is also a well-known arena for applying operations research methodology. Traditionally divided in three main parts, the works within this area have dealt with fish stock and harvesting, fish processing, and marketing. Recently, the focus has shifted to integrated planning, where fishing fleet operations are combined with plant processing. Currently, a broader view of the supply chain needs to be adopted as many companies in this industrial sector are striving to improve their capacity utilizations, operational efficiency, and profitability. Thus, both upstream and downstream uncertainties have to be handled. While it has been recognized that decision flexibility can be used to manage supply chain uncertainty, no known stochastic modeling formulations have explicitly accounted for it in fish processing.To address the described planning challenges, this paper develops an integral stochastic model, incorporating both upstream (raw material quantities) and downstream (finished goods market prices) uncertainties, while accounting for fish quality deterioration and shelf-life restrictions. It is then tested, estimating the potential economic value of flexibility in the supply chain provided by the introduction of super-chilling technologies and application of the described stochastic formulation. This way, it reflects a triangulation of technological development, operational efficiency, and market profitability. Thus, it is a unique opportunity to address the real-world complexity and enhance the body of knowledge in operations research. (C) 2014 Elsevier B.V. All rights reserved.

We present a modeling framework for analyzing if the use of interruptible transportation services can improve capacity utilization in a natural gas transportation network. The network consists of two decision makers: the transmission system operator (TSO) and a shipper of natural gas. The TSO is responsible for the routing of gas in the network and allocates capacity to the shipper to ensure that the security of supply in the network is within given bounds. The TSO can offer two different types of transportation services: firm and interruptible. Only firm services have a security of supply measure, while the interruptible services can freely be interrupted whenever the available capacity in the transportation network is not sufficiently large. We apply our modeling framework on a case study with realistic data from the Norwegian Continental Shelf. The results indicate substantial increased throughput and profits with the introduction of interruptible services. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

In this chapter we provide an introduction to natural gas networks. We describe the different network elements and some physical aspects of natural gas transportation from producer to market. In particular, we focus on the special characteristics of natural gas transportation systems that lead to system effects and challenges for analyses. We also categorize and outline typical optimization problems encountered when analyzing natural gas networks on a strategic, tactical, or operational horizon. In addition, we discuss how to handle uncertainty in this setting.

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