• Energy Research

This paper studies the dynamic application of the minimax regret (MR) decision criterion to identify robust flood risk management strategies under climate change uncertainty and emerging information. An MR method is developed that uses multiple learning scenarios, for example about sea level rise or river peak flow development, to analyse effects of changes in information on optimal investment in flood protection. To illustrate the method, optimal dike height and floodplain development are studied in a conceptual model, and conventional and adaptive MR solutions are compared. A dynamic application of the MR decision criterion allows investments to be changed after new information on climate change impacts, which has an effect on today’s optimal investments. The results suggest that adaptive MR solutions are more robust than the solutions obtained from a conventional MR analysis of investments in flood protection. Moreover, adaptive MR analysis with multiple learning scenarios is more general and contains conventional MR analysis as a special case.

The efficient control of electrical vehicles may contribute to sustainable use of energy. In recent studies, a model has been analyzed and several algorithms based on branch and bound have been presented. In this work, we discuss a reformulated model on the control of an electric vehicle based on the minimization of the energy consumption during an imposed displacement. We will show that similar results can be obtained by applying standard software. Moreover, this paper shows that the specified control problem can be handled from a dynamic programming perspective.

The main objective of this study was to develop minimum cost supply strategies for willow to energy plants (two plant sizes: 0.5 and 30 MWe, two energy conversion technologies: combustion and gasification). Time span between harvest and energy conversion varied from 1 to 12 months. For a realistic comparison, different supply chains were based on the same initial characteristics (i.e., moisture content 50% wb at harvest) and final fuel specifications at the energy plant (moisture content 20% wb, particle size chips or chunks). Cost calculations were based on the integral cost calculation method and were presented for all process steps. The main conclusion was that the time span between harvest and energy conversion and the size and conversion technology of the energy plant largely influence the design of the supply chain and consequently the supply costs. The fuel supply costs ranged from 17.6 to 26.1 ECU/t DM (where DM stands for oven dry matter) or 0.010 to 0.023 ECU/kWh. The cost reduction which could be achieved by choosing the minimum cost chain design could be as high as 45% or 14.4 ECU/t DM. Generally, the strategy of minimum costs for supply of fuel to an energy plant running all year round on willow was as follows: … for farmers who should supply their willow within 6 months after harvest: harvest as chips, forced drying at the farm and transport (if necessary); … for farmers who should supply their willow beyond 6 months after harvest: harvest as chunks or stems, natural drying near the willow field, transport (if necessary) and central chipping (if applicable).

Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet composition is decided and on the lower level the maintenance operations are scheduled for a set of weather and breakdown scenarios. A drawback of deciding an a priori information schedule for the coming year is that, the weather outcomes and breakdowns are not known in advance. Consequently, given a fleet composition, its corresponding maintenance costs are underestimated compared to what can be realised in practice under incomplete information. Therefore, we present a heuristic that simulates the practical scheduling and may provide a better cost estimate. The latter method is used to evaluate a fleet composition based on available information and it is compared with the MILP solution based on a priori information.

Decision-making often refers to ranking alternatives based on many involved criteria. Since the introduction of the Analytic Hierarchy Process (AHP) in 1980, pairwise comparisons of criteria have a long tradition in multi-criteria decision-making. One of the main concerns of the AHP refers to the inconsistency of decision makers in pairwise comparisons. Recently, the Best-Worst Method (BWM) was introduced to reduce the inconsistency by a concept that needs substantially less pairwise comparisons. The BWM includes solving a non-linear model (NLM) to derive the weights from the comparisons. A linear model (LM) was introduced in a follow-up to approximate the original NLM. This paper shows that the optimal weights of the proposed linear model (LM) may differ substantially from the optimal weights of the original NLM model. Moreover, this paper provides an MILP model approximation (MILM) which can be solved by standard optimization software and illustrates that its solution approximates the optimal weights of the original NLM model arbitrarily close. Since consistency in pairwise comparisons is usually not self-evident in practice, using approximation MILM to derive unique solutions of the original NLM, extends the applicability of the Best-Worst Method.

North Sea fisheries are managed by the European Union (EU) through a system of annual quota. Due to uncertainty about future fish stocks, yearly revisions of these policies lead to fluctuation in quota, which in turn affects harvest and investment decisions of fishermen. Determination of quota requires high management costs in terms of obtaining information and negotiations between experts and policy makers. To reduce both quota fluctuation and management costs, the EU has proposed a system of multiannual quota. In this paper we study the effect of multiannual quota on quota volatility and resource rents, while accounting for management costs. We develop a bi-level stochastic dynamic programming model, where at level one, the EU determines the quota that maximizes resource rents. At level two, fishermen decide myopically on their harvest and investment levels, subject to the quota. Results show that policy makers can reduce quota volatility and improve resource rents from the fishery with multiannual quota. Important trade-offs are involved in the accomplishment of these objectives: fish stock and investments become more volatile, which leads to more overcapacity.

Stormwater drainage and other water systems are vulnerable to changes in rainfall and runoff and need to be adapted to climate change. This paper studies impacts of rainfall variability and changing return periods of rainfall extremes on cost-effective adaptation of water systems to climate change given a predefined system performance target, for example a flood risk standard. Rainfall variability causes system performance estimates to be volatile. These estimates may be used to recurrently evaluate system performance. This paper presents a model for this setting, and develops a solution method to identify cost-effective investments in stormwater drainage adaptations. Runoff and water levels are simulated with rainfall from stationary rainfall distributions, and time series of annual rainfall maxima are simulated for a climate scenario. Cost-effective investment strategies are determined by dynamic programming. The method is applied to study the choice of volume for a storage basin in a Dutch polder. We find that 'white noise', i.e. trend-free variability of rainfall, might cause earlier re-investment than expected under projected changes in rainfall. The risk of early re-investment may be reduced by increasing initial investment. This can be cost-effective if the investment involves fixed costs. Increasing initial investments, therefore, not only increases water system robustness to structural changes in rainfall, but could also offer insurance against additional costs that would occur if system performance is underestimated and re-investment becomes inevitable.