Evaluation of open photovoltaic and wind production time series for Norwegian locations

We investigate the accuracy of wind and photovoltaic time series in individual systems in Norway. To study the accuracy of the available open data sets, we compare the measured production from individual photovoltaic- and wind power plants to the open time series from Renewables.ninja and EMHIRES. Additionally, we try to adjust the wind speed based on the average wind speed from Global Wind Atlas 3.0 and Norwegian water resources and energy directorate's Wind Map to try to achieve more accurate wind speed time series that take into account the local wind conditions, since they are not well represented in the large resolution of the MERRA-2 data set used by Renewables.ninja. The results for photovoltaic production time series are promising, the correlation between production obtained from Renewables.ninja and measured production is above 0.72 and maximum capacity factor difference of 2.5%. For the case of wind production, production time series show considerable deviations depending on the specific wind farm (correlation between 0.51 and 0.91 depending on the case and year). Additionally, the adjustments only improve the time series in some of the wind farms, whereas in others the results are even less accurate than the Renewables.ninja time series compared to the measured data.publishedVersio

Stochastic master surgery scheduling

The aim of the Master Surgery Scheduling Problem (MSSP) is to schedule the medical specialties to the different operating rooms available, such that surgeries may be performed efficiently. We consider a MSSP where elective and emergency patients can be treated in the same operating rooms. In addition to elective-dedicated operating room slots, flexible operating room slots are introduced to handle the fluctuating demand of emergency patients. To solve the MSSP, we propose a simulation-optimization approach consisting of a two-stage stochastic optimization model and a discrete-event simulation model. For the two-stage stochastic optimization model, uncertain arrivals of emergency patients are represented by discrete scenarios. The discrete-event simulation model is developed to address uncertainty related to the surgery duration and the length of stay at the hospital, and to test the Master Surgery Schedule (MSS) developed by the optimization model in a stochastic operational-level environment. In addition, the simulation model is used to generate scenarios for the optimization model. We present some general advice for surgery scheduling based on testing the optimization model in a numerical study. The simulation-optimization approach is applied to a case study from a hospital department that treats both elective and emergency patients. The optimized MSS outperforms the manually generated MSS, both in terms of emergency waiting time for surgery, and emergency interruptions to the flow of electives.acceptedVersio

Patient-tailored levothyroxine dosage with pharmacokinetic/pharmacodynamic modeling: A novel approach after total thyroidectomy

Background: After seven decades of levothyroxine (LT4) replacement therapy, dosage adjustment still takes several months. We have developed a decision aid tool (DAT) that models LT4 pharmacometrics and enables patient-tailored dosage. The aim of this was to speed up dosage adjustments for patients after total thyroidectomy. Methods: The DAT computer program was developed with a group of 46 patients post-thyroidectomy, and it was then applied in a prospective randomized multicenter validation trial in 145 unselected patients admitted for total thyroidectomy for goiter, differentiated thyroid cancer, or thyrotoxicosis. The LT4 dosage was adjusted after only two weeks, with or without application of the DAT, which calculated individual free thyroxine (fT4) targets based on four repeated measurements of fT4 and thyrotropin (TSH) levels. The individual TSH target was either <0.1, 0.1–0.5, or 0.5–2.0 mIU/L, depending on the diagnosis. Initial postoperative LT4 dosage was determined according to clinical routine without using algorithms. A simplified DAT with a population-based fT4 target was used for thyrotoxic patients who often went into surgery after prolonged TSH suppression. Subsequent LT4 adjustments were carried out every six weeks until target TSH was achieved. Results: When clinicians were guided by the DAT, 40% of patients with goiter and 59% of patients with cancer satisfied the narrow TSH targets eight weeks after surgery, as compared with only 0% and 19% of the controls, respectively. The TSH was within the normal range in 80% of DAT/goiter patients eight weeks after surgery as compared with 19% of controls. The DAT shortened the average dosage adjustment period by 58 days in the goiter group and 40 days in the cancer group. For thyrotoxic patients, application of the simplified DAT did not improve the dosage adjustment. Conclusions: Application of the DAT in combination with early postoperative TSH and fT4 monitoring offers a fast approach to LT4 dosage after total thyroidectomy for patients with goiter or differentiated thyroid cancer. Estimation of individual TSH-fT4 dynamics was crucial for the model to work, as removal of this feature in the applied model for thyrotoxic patients also removed the benefit of the DAT

Managing Uncertainty in Design and Operation of Natural Gas Infrastructure

Summary of the thesis: This thesis concerns the management of uncertainty in design and operation of natural gas infrastructure by means of mathematical programming. The export of natural gas is an important industry in Norway. Investments in infrastructure such as subsea pipelines and processing facilities with long lifetime are capital intensive and mandate thorough analysis of future cash flows to assess profitability. Several crucial parameters are uncertain, such as resources, costs and future prices, and analysis of net present value should consider this uncertainty carefully. The long lifetime with significant short-term uncertainty and variability in addition to long-term uncertainty makes this particularly challenging. Natural gas is not a homogenous commodity, and different composition of gas from each field makes the management of gas quality an important consideration. Gas quality may be altered through mixing in the transport pipeline network, or in processing facilities. Accounting for pooling in the network with quality constraints introduces computationally expensive non-convex formulations. The main contributions of the work in this thesis consists of modelling short- and long-term uncertainty for design and operation of natural gas networks in models that combine these demands and introduce multi-horizon stochastic programming. The pooling problem is addressed by a novel discretization scheme and auxiliary linear programs that will improve solution times in many instances. A generalized global optimization multi-commodity pooling formulation with processing facilities and composite quality constraints is appropriate for analysis in this context. Traditionally, analysts consider uncertainty as exogenous, i.e. unaffected by the decisions in the model. In this thesis, the work on stochastic programming with endogenous uncertainty is reviewed with an expanded taxonomy, with several novel models with decision-dependent probabilities. This demonstrates how this problem may be addressed through the framework of stochastic programming with recourse

Decision-dependent probabilities in stochastic programs with recourse

Stochastic programming with recourse usually assumes uncertainty to be exogenous. Our work presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, we present models where the parameters of the probability distribution are first-stage decision variables. The probability distributions are either incorporated in the model using the exact expression or by using a rational approximation. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching

Optimization Model to Analyse Optimal Development of NaturalGas Fields and Infrastructure

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

Evaluation of open photovoltaic and wind production time series for Norwegian locations

publishedVersio

Multi-horizon stochastic programming

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.publishedVersio

Decision-dependent probabilities in stochastic programs with recourse

Stochastic programming with recourse usually assumes uncertainty to be exogenous. Our work presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, we present models where the parameters of the probability distribution are first-stage decision variables. The probability distributions are either incorporated in the model using the exact expression or by using a rational approximation. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching

Stochastic master surgery scheduling

The aim of the Master Surgery Scheduling Problem (MSSP) is to schedule the medical specialties to the different operating rooms available, such that surgeries may be performed efficiently. We consider a MSSP where elective and emergency patients can be treated in the same operating rooms. In addition to elective-dedicated operating room slots, flexible operating room slots are introduced to handle the fluctuating demand of emergency patients. To solve the MSSP, we propose a simulation-optimization approach consisting of a two-stage stochastic optimization model and a discrete-event simulation model. For the two-stage stochastic optimization model, uncertain arrivals of emergency patients are represented by discrete scenarios. The discrete-event simulation model is developed to address uncertainty related to the surgery duration and the length of stay at the hospital, and to test the Master Surgery Schedule (MSS) developed by the optimization model in a stochastic operational-level environment. In addition, the simulation model is used to generate scenarios for the optimization model. We present some general advice for surgery scheduling based on testing the optimization model in a numerical study. The simulation-optimization approach is applied to a case study from a hospital department that treats both elective and emergency patients. The optimized MSS outperforms the manually generated MSS, both in terms of emergency waiting time for surgery, and emergency interruptions to the flow of electives