Liquidity risks on power exchanges

Financial derivatives are important hedging tool for asset’s manager. Electricity is by its very nature the most volatile commodity, which creates big incentive to share the risk among the market participants through financial contracts. But, even if volume of derivatives contracts traded on Power Exchanges has been growing since the beginning of the restructuring of the sector, electricity markets continue to be considerably less liquid than other commodities. This paper tries to quantify the effect of this insufficient liquidity on power exchange, by introducing a pricing equilibrium model for power derivatives where agents can not hedge up to their desired level. Mathematically, the problem is a two stage stochastic Generalized Nash Equilibrium and its solution is not unique. Computing a large panel of solutions, we show how the risk premium and player’s profit are affected by the illiquidity.illiquidity, electricity, power exchange, artitrage, generalized Nash Equilibrium, equilibrium based model, coherent risk valuation

Integrating the Cost of Quality into Multi-Products Multi-Components Supply Chain Network Design

More than ever before the success of a company heavily depends on its supply chain and how efficient the network. A supply chain needs to be configured in such a manner as to minimize cost while still maintaining a good quality level to satisfy the end user and to be efficient, designing for the network and the whole chain is important. Including the cost of quality into the process of designing the network can be rewording and revealing. In this research the concept of cost of quality as a performance measure was integrated into the supply chain network designing process for a supply chain concerned with multi products multi components. This research discusses how this supply chain can be mathematically modeled, solutions for the resulted model and finally studied the effect of the inclusion of the quality as a parameter on the result of the deigning process. Nonlinear mixed integer mathematical model was developed for the problem and for solving the model two solutions based on Genetic algorithm and Tabu Search were developed and compared. The results and analysis show that the solution based on the Genetic algorithm outperforms the Tabu Search based solution especially in large size problems. In addition, the analysis showed that the inclusion of the cost of quality into the model effect the designing process and changes the resultant routes

Coordination of a Retail Supply Chain Distribution Flow

Retail supply chains are very sensitive by their nature and need to adapt to several situations with the aim to increase their reliability, flexibility and convenience. There are many factors affecting the effectiveness of a distribution flow, from perishability, capacities of storage areas, lead times, untimely deliveries and others. Because the latter heavily depend on the planned and realized distribution and not on the demand side perspective, we partially neglect them in the initial study. We focus only on the demand satisfaction, without considering any pricing policies, perishability factors, etc. Beside stochastic demand modelling we introduce the multi-objective optimization approach to cope with the minimization of transport and warehouse costs, minimization of over stock effects and the maximization of customers’ service level. Methodology used produces a set of solutions and its quality estimations in order to find the desired distribution plan that is near optimal. The paper further explains the integration of management decisions with respect to the obtained results of the modelling approach. The applicability of the model will be explained using a numerical example

Multi-energy retail market simulation with autonomous intelligent agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

Supply Chain Coordination with Carbon Trading Price and Consumers’ Environmental Awareness Dependent Demand

Carbon emissions reduction in supply chain is an effective method to reduce the greenhouse effect. The paper investigates the impacts of carbon trading price and consumers’ environmental awareness on carbon emissions in supply chain under the cap-and-trade system. Firstly, it analyzes the centralized decision structure and obtains the requirements to coordinate carbon emissions reduction and order quantity in supply chain. Secondly, it proposes the supply chain coordination mechanism with revenue-sharing contract based on quantity discount policy, and the requirements that the contract parameters need to satisfy are also given. Thirdly, assuming the market demand is affected by consumer’s environmental awareness in addition form, the paper proposes the methods to determine the optimal order quantity and the optimal level of carbon emissions through model optimization. Finally, it investigates the impacts of carbon trading price on carbon emissions in supply chain. The results show that clean manufacturer’s optimal per-unit carbon emissions increase as the carbon trading price increases, while nongreen manufacturer’s optimal per-unit carbon emissions decrease as the carbon trading price increases. For the middle emissions manufacturer, the optimal per-unit carbon emissions depend on the relationship between the carbon trading price and the carbon reduction coefficient

Security of supply and retail competition in the European gas market: Some model-based insights

In this paper, we analyze the impact of uncertain disruptions in gas supply upon gas retailer contracting behavior and consequent price and welfare implications in a gas market characterized by long-term gas contracts using a static Cournot model. In order to most realistically describe the economical situation, our representation divides the market into two stages: the upstream market that links, by means of long-term contracts, producers in exporting countries (Russia, Algeria, etc.) to local retailers who bring gas to the consuming countries to satisfy local demands in the downstream market. Disruption costs are modeled using short-run demand functions. First we mathematically develop a general model and write the associated KKT conditions, then we propose some case studies, under iso-elasticity assumptions, for the long- short-run inverse-demand curves in order to predict qualitatively and quantitatively the impacts of supply disruptions on Western European gas trade. In the second part, we study in detail the German gas market of the 1980s to explain the supply choices of the German retailer, and we derive interesting conclusions and insights concerning the amounts and prices of natural gas brought to the market. The last part of the paper is dedicated to a study of the Bulgarian gas market, which is greatly dependent on the Russian gas supplies and hence very sensitive to interruption risks. Some interesting conclusions are derived concerning the necessity to economically regulate the market, by means of gas amounts control, if the disruption probability is high enough

Dynamic decision making under uncertainty in renewable energy portfolio management and inventory management

It is challenging and important for a firm to make effective decisions under uncertainties, such as random fluctuations of products prices or demands, etc. This dissertation formulates mathematic models to help decision makers in energy and retail industries make optimal timing and optimal operational decisions when facing uncertain electricity prices and demands. As for energy portfolio management, the optimal entry and dispatch strategies are investigated for an electricity generating firm to introduce a renewable power plant as an alternative method for generating electricity, with or without construction delay. In addition, the abandonment strategies of considering shutting down one of the two power plants in the energy portfolio are studied. To develop these strategies, the expected per unit profit is maximized over a finite time horizon by assuming that the price of electricity follows mean reversion stochastic process. This problem is formulated as a mixed optimal stochastic control and optimal stopping problem. The original problem is solved numerically through two auxiliary problems. Numerical experiments are conducted to confirm the results. The sensitivity analysis of the parameters is conducted to reveal how the uncertainty of electricity price, investment, operation cost, and production rate affect the decisions. A dynamic inventory model is also developed to study optimal control policy in a finite planning horizon with consideration of debt financing and tax. The model assumes that the retailer raises funds from the financial market and replenishes its stock under the constraint of its cash flow facing random demand. The objective is to maximize the expected terminal wealth. The optimal inventory policy and the optimal debt financing decision with the capital constraint and the effect of tax are obtained --Abstract, page iii

Integrated Supply Chain Network Design: Location, Transportation, Routing and Inventory Decisions

abstract: In this dissertation, an innovative framework for designing a multi-product integrated supply chain network is proposed. Multiple products are shipped from production facilities to retailers through a network of Distribution Centers (DCs). Each retailer has an independent, random demand for multiple products. The particular problem considered in this study also involves mixed-product transshipments between DCs with multiple truck size selection and routing delivery to retailers. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transshipments and routing are involved. In order to find out a good solution effectively, a two-phase solution methodology is derived: Phase I solves an integer programming model which includes all the constraints in the original model except that the routings are simplified to direct shipments by using estimated routing cost parameters. Then Phase II model solves the lower level inventory routing problem for each opened DC and its assigned retailers. The accuracy of the estimated routing cost and the effectiveness of the two-phase solution methodology are evaluated, the computational performance is found to be promising. The problem is able to be heuristically solved within a reasonable time frame for a broad range of problem sizes (one hour for the instance of 200 retailers). In addition, a model is generated for a similar network design problem considering direct shipment and consolidation within the same product set opportunities. A genetic algorithm and a specific problem heuristic are designed, tested and compared on several realistic scenarios.Dissertation/ThesisPh.D. Industrial Engineering 201

A new hybrid GA-PSO method for solving multi-period inventory routing problem with considering financial decisions

Integration of various logistical components in supply chain management, such as transportation, inventory control and facility location are becoming common practice to avoid sub-optimization in nowadays’ competitive environment. The integration of transportation and inventory decisions is known as inventory routing problem (IRP) in the literature. The problem aims to determine the delivery quantity for each customer and the network routes to be used in each period, so that the total inventory and transportation costs are to be minimized. On the contrary of conventional IRP that each retailer can only provide its demand from the supplier, in this paper, a new multi-period, multi-item IRP model with considering lateral trans-shipment, back-log and financial decisions is proposed as a business model in a distinct organization. The main purpose of this paper is applying an applicable inventory routing model with considering real world setting and solving it with an appropriate method.Peer Reviewe