Procurement risk management in a petroleum refinery.

We analyze a petroleum refinery's procurement strategy, explaining how risk management affects optimal sourcing from long-term, spot, and swap contracts. We use time series analysis to model the interaction between petroleum prices, transportation costs, and gross product worth. These models are then used to generate the scenarios incorporated in the stochastic program applied to compute the conditional value-at-risk. We prove the necessary and sufficient conditions for the optimal procurement and risk management strategies, and show that risk aversion can be better represented by the weighted average between expected profit and conditional value-at-risk, deriving the respective ISO curves. We estimate that an increase in the degree of risk aversion decreases the use of swap contracts. Our model is applied to the analysis of a refinery based in Singapore. Using regression analysis, we show we cannot reject the hypothesis of a statistically significant relationship between the way Saudi Arabia prices the long-term contracts and the shape of the forward curve. We then study how risk aversion influences the procurement strategies, profitability, and risk exposure of the refinery. Finally, we analyze the pricing of long-term (forward) contracts by Saudi Arabia, and study how the country could benefit from a different pricing policy

Physical vs Virtual Corporate Power Purchase Agreements: Meeting Renewable Targets Amid Demand and Price Uncertainty

Power purchase agreements (PPAs) have become an important corporate procurement vehicle for renewable power, especially among companies that have committed to targets requiring a certain fraction of their power demand be met by renewables. PPAs are long-term contracts that provide renewable energy certificates (RECs) to the corporate buyer and take two main forms: Physical vs Virtual. Physical PPAs deliver power in addition to RECs, while virtual PPAs are financial contracts that hedge (at least partially) power price uncertainty. We compare procurement portfolios that sign physical PPAs with ones that sign virtual PPAs, focusing on fixed-volume contracts and emphasizing uncertainties in power demand and the prices of power and RECs. In particular, we first analyze a two-stage stochastic model to understand the behavior of procurement quantities and costs when using physical and virtual PPAs as well as variants that limit risk. We subsequently formulate a Markov decision process (MDP) that optimizes the multi-stage procurement of power to reach and sustain a renewable procurement target. By leveraging state-of-the-art reoptimization techniques, we solve this MDP on realistic instances to near optimality, and highlight the relative benefits of using PPA types to meet a renewable target

Renewable power and electricity prices: the impact of forward markets

Increasing variable renewable power generation (e.g., wind) is expected to reduce wholesale electricity prices by virtue of its low marginal production cost. This merit-order effect of renewables displacing incumbent conventional (e.g., gas) generation forms the theoretical underpinning for investment decisions and policy in the power industry. This paper uses a game-theoretic market model to investigate how intermittently available wind generation affects electricity prices in the presence of forward markets, which are widely used by power companies to hedge against revenue variability ahead of near-real-time spot trading. We find that in addition to the established merit-order effect, renewable generation affects power prices through forward-market hedging. This forward effect reinforces the merit-order effect in reducing prices for moderate amounts of wind generation capacity but mitigates or even reverses it for higher capacities. For moderate wind capacity, uncertainty over its output increases hedging, and these higher forward sales lead to lower prices. For higher capacities, however, wind variability conversely causes power producers to behave less aggressively in forward trading for fear of unfavorable spot-market positions. The lower sales counteract the merit-order effect, and prices may then paradoxically increase with wind capacity despite its lower production cost. We confirm the potential for such reversals in a numerical study, suggesting new empirical questions while providing potential explanations for previously contradictory observed effects of market fundamentals. We conclude that considering the conventional merit-order effect alone is insufficient for evaluating the price impacts of variable renewable generation in the presence of forward markets

Supply Management in Multiproduct Firms with Fixed Proportions Technology

This paper studies the supply management of a primary input, where this input gives rise to multiple products in fixed proportions. My objective is twofold. First, I study fixed proportions technology under demand uncertainty in comparison with the flexible and dedicated technologies. I show that fixed proportions technology has a cost-pooling value over dedicated technology, which is larger than the capacity-pooling value of flexible technology over dedicated technology. I identify the critical role that demand correlation plays with the fixed proportions technology: in contrast to the capacity-pooling value, which decreases in demand correlation, the cost-pooling value increases in demand correlation. Second, focusing on the fixed proportions technology, I study supply management in the presence of contract and spot markets. I investigate how the optimal supply management strategy should respond to changing market uncertainties, and the differences in this response based on the contract type. I find that when the exercise price of the contract is high, a higher contract market dependence is the best response to the increasing demand correlation or spot price variability. However, a lower contract market dependence is the best response to the same when the exercise price is low. Managerially, these results are important because they imply that the supply management strategy adopted as a response to a change in the business environment should differ depending on the contract type. My results have implications about the new product strategy and the procurement contract choice of the processors in the agricultural industries. This paper was accepted by Yossi Aviv, operations management. </jats:p

Cyber-Physical Systems Design: Electricity Markets and Network Security

This thesis presents Cyber-Physical Systems Design (CPS Design). Design of CPS is challenging and requires interdisciplinary studies of engineering and economics because of the distinguishing features of CPS: strategic (self profit-maximizing) decision makers, complex physical constraints, and large-scale networked systems. We study these features by focusing on designing markets with complex constraints including both policy and physical constraints, and decomposing large-scale CPS within the context of electricity markets and network security. We first study market design for implementation of complex electricity policy targets, i.e. sustainability, reliability, and price efficiency, by efficient design of spot, carbon, and capacity markets that correct the deficiencies of the current electricity markets; this design does not take into account the network constraints due to the Kirchhoff's laws. To address this problem, we develop a framework based on the design of efficient auctions with constraints. Our market design sheds light on major debates in electricity policy including capacity-and-energy vs energy-only markets, carbon market vs carbon tax, and use of price or offer caps. Second, we add network constraints due to Kirchhoff's laws of current and voltage, which are unique to electricity networks, to the design of electricity spot markets with complex physical constraints. To address this problem, we develop a framework for the design of networked markets based on the ideas from local public goods. Finally, we study the design of defense policies for large-scale network security. Our approach is to design approximately optimal defense policies that are computable. We develop a framework based on the notion of influence graph, which captures the connectivity of the security states of the system elements, to decompose the system into subsystems. We then design approximately optimal defense policies for each sub-system. We consider non-Bayesian uncertainty and even though we do not model the attacker as a strategic decision maker, we compensate (in part) for the lack of this feature by adopting a minmax performance criterion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144165/1/rasouli_1.pd

Optimal Trading of a Storable Commodity via Forward Markets

A commodity market participant trading via her inventory has access to both spot and forward markets. To liquidate her inventory, she can sell at the spot price, take a short forward position, or do a combination of both. A trade is proposed in which there is always a hedging forward contract, which can be considered a dynamic cash and carry arbitrage. The trader can adjust the maturity of the forward contract dynamically until the inventory is depleted or a time constraint is reached. In the first setup, the storage contract (to carry inventory) is assumed to have a constant cost and a flexible duration. The risk and return characteristics of an Approximate Dynamic Programming (ADP) and a Forward Dynamic Optimization solution are compared. The trade is contrasted with optimal spot sale among other alternative liquidation strategies. Independent from the underlying stochastic forward price model, it is proved and verified numerically that a partial sale strategy is not optimal. The optimally selected forward maturities are limited to the subset comprising the immediate, next, and last timesteps. Under a more realistic storage contract, which assumes a stochastic cost and a fixed duration, a new ADP approach is developed. The optimal policy shows the tanker rent decision is accompanied by a buy order since the loss from an empty tanker is more than the gain of renting it cheaply yet early. Given the nonadjustable duration of the rent contract, a longer contract generates a higher value by benefiting from a tanker refill option

Essays on Stochastic Inventory Systems

University of Minnesota Ph.D. dissertation. July 2015. Major: Industrial and Systems Engineering. Advisor: Saif Benjaafar. 1 computer file (PDF); ix, 147 pages.This thesis consists of three essays in stochastic inventory systems. The first essay is on the impact of input price variability and correlation on stochastic inventory systems. For a general class of such systems, we show that the expected cost function is concave in the input price. From this, it follows that higher input price variability in the sense of the convex order always leads to lower expected cost. We show that this is true under a wide range of assumptions for price evolution, including cases with i.i.d. prices and cases where prices are correlated and evolve according to an AR(1) process, a geometric Brownian motion, or a Markovian martingale. In addition, the result holds in cases where there is just a single period. We also examine the impact of price correlation over time and across inputs, and we find that expected cost is increasing in price correlation over time and decreasing in price correlation across components. We present results of a numerical study that provide insights on how various parameters influence the effects of price variability and correlation. The second essay is on the optimal control of inventory systems with stochastic and independent leadtimes. We show that a fixed base-stock policy is sub-optimal and can perform poorly. For the case of exponentially distributed leadtimes, we show that the optimal policy is state-dependent and specified in terms of an inventory-dependent threshold function. Moreover, we show that this threshold function is non-increasing in the inventory level and characterized by at most m parameters. That is, once the threshold function starts to decrease it continues to decrease with a rate that is at least one. Taking advantage of this structure, we develop an efficient algorithm for computing these parameters. In characterizing the structure of the optimal policy, we rely on an application of the Banach fixed point theorem. We compare the performance of the optimal policy to that of simpler heuristics. We also extend our analysis to systems with lost sales and systems with order cancellations. The third essay is on the optimal policies for inventory systems with concave ordering costs. By extending the Scarf (1959} model to systems with piecewise linear concave ordering costs, we characterize the structure of optimal policies for periodic review inventory systems with concave ordering costs and general demand distributions. We show that, except for a bounded region, the generalized (s,S) policy is optimal. We do so by (a) introducing a conditional monotonicity property for the optimal order-up-to levels and (b) applying the notion of c-convexity. We also provide conditions under which the generalized (s, S) policy is optimal for all regions of the state space

Market Risks and Strategies in Power Systems Integrating Renewable Energy

Energy businesses are going through a series of swift and radical transformations to meet the growing demands for sustainable energy. The integration of wind and solar introduces more low marginal costs suppliers to power markets, as no fuels are needed to produce electricity. Most power produced by renewable energy sources is however variable and difficult to predict by nature, putting current power system operations under pressure and causing prices to fluctuate heavily. Increased competition, new production technologies and volatile prices completely changed operations in today’s power markets. In this dissertation, we assess the integration of intermittent renewable energy sources in relation to agents' risk preferen

Optimal Energy Procurement in Spot and Forward Markets

Storage capacity for energy, such as electricity, natural gas, and oil, is limited. Thus, spot and forward purchases for delivery on the usage date play an important role in matching the supply and the uncertain demand of energy. Transaction costs tend to be larger in spot than forward energy, and more generally commodity, markets. Hence, partially procuring supply in the forward market, rather than entirely in the spot market, is a potentially valuable real option. We call this option the forward procurement option. The study of this option from the perspective of differential transaction costs has received little attention in the literature. We thus formulate and analyze a parsimonious procurement model with differential spot and forward transaction costs and correlated spot demand and nominal price random variables. Our analysis, in part based on natural gas data, sheds novel light on the value of the forward procurement option and its optimal exercise, as well as their sensitivities to parameters of interest. Our main insight is that procuring the demand forecast in the forward market is nearly optimal on the instances that we consider. This greatly simplifies the management of this option. We obtain analogous results with a richer model in which the supply procured in the forward market is delivered at multiple dates. Beyond energy, our research has potential relevance for the procurement of other commodities, such as metals and agricultural products.</p