Antonio Violi

Dynamic pricing of electricity in retail markets

This paper aims at defining a dynamic and flexible tariff structure for a distribution company that protects the retail consumers against the excessive fluctuations of the wholesales market prices. We propose a two-stage pricing scheme that sets in a first-stage a time-of-use tariff that is corrected later by a dynamic component once the real-time demand has been observed. A personalized tariff scheme may be offered by a distribution company to each dynamic customer by allowing him to choose the appropriate robustness level expressed in terms of variability between the first and the second-stage decisions. The arising limited recourse model has been tested on realistic test problems, by using a slight modification of a recently proposed interior point solution framework.

A two-stage stochastic programming model for electric energy producers

The bilateral contract selection and bids definition constitute a strategic issue for electric energy producers that operate in competitive markets, as the liberalized electricity ones. In this paper we propose a two-stage stochastic integer programming model for the integrated optimization of power production and trading which include a specific measure accounting for risk management. We solve the model by means of a novel enumerative solution approach that exploits the particular problem structure. Finally, we report some preliminary computational experiments.

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.

Constrained auction clearing in the Italian electricity market

Abstract.Most of the liberalized electricity systems use the auction as a market model. The complexity of the underlying optimization formulation depends on the technical and regulatory constraints that must be considered. In Italy, the auction clearing should include not only congestion management limitations, but also a challenging regulatory constraint imposing that, while the zonal prices are allowed on the selling side, a uniform purchasing price has to be applied for all the zones of the Italian system. Such constraint introduces several complexities such as non-linearity and integrality. In this paper we discuss the modeling issues arising in the Italian context and we propose, in addition, a mechanism for the priority management of the offers/bids acceptance. We test the behavior of the models developed on a set of problems that represent all the possible scenarios that can be met in practice. The numerical results demonstrate the validity and the effectiveness of the proposed models.

Capital rationing problems under uncertainty and risk

Capital rationing is a major problem in managerial decision making. The classical mathematical formulation of the problem relies on a multi-dimensional knapsack model with known input parameters. Since capital rationing is carried out in conditions where uncertainty is the rule rather than the exception, the hypothesis of deterministic data limits the applicability of deterministic formulations in real settings. This paper proposes a stochastic version of the capital rationing problem which explicitly accounts for uncertainty. In particular, a mathematical formulation is provided in the framework of stochastic programming with joint probabilistic constraints and a novel solution approach is proposed. The basic model is also extended to include specific risk measures. Preliminary computational results are presented and discussed.

Optimization Models for Determining Performance Benchmarks in Wireless Sensor Networks

In this paper we propose some innovative optimization models for Wireless Sensor Networks. The models are chosen depending on the task the network is called to execute and they focus on the optimization of some specific performance objectives. Indeed, starting from a generic configuration, the optimal solution defines a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. Controlled mobility of the nodes is used for reaching the wanted displacement. The behavior of the proposed models has been evaluated in comparison with a distributed algorithm on the basis of an extensive computational study and by considering different scenarios.

Managing Price Risk while bidding in a Multimarket Environment

In the deregulated marketplace, generation companies sell energy through auctions in a daily market. The daily-price volatility, together with the bids acceptance process uncertainty, make arise the need of performing risk assessment. A a multi-stage mixed-integer stochastic programming model with linear constraints, able to detect the profitability and risk of bidding in a multi auction energy market, is proposed for power producers. At this purpose, a particularly effective risk measure as the Conditional Value at Risk has been chosen: a discrete formulation has been proposed and evaluated for quantifying the risk of a producer portfolio. The intuitive scenario tree formulation has been adopted to represent the evolution of the random clearing prices and quantities. The Italian zonal pricing has been used as pricing system, to manage network congestions. The proposed model so provides suppliers with an efficient tool able to handle daily market-price uncertainties and to capture, in powerful aggregate risk measures, all relevant portfolio effects of market-risk exposure. Simulations are carried out in a 24-hour time frame on a representative test problem.

Short-term forecasting of day-ahead electricity market price

A very important task in electricity market operation is to forecast day ahead market price in order to implement adequate bidding strategies. In this direction, this paper proposes a technique to forecast day-ahead electricity prices based on the mean reverting process, using a properly fitted model. Results from the electricity market of Italy during year 2009 are finally reported.

The Optimal Energy Procurement Problem: A Stochastic Programming Approach

The paper analyzes the problem of the optimal procurement plan at a strategic level for a set of prosumers aggregated within a coalition. Electric energy needs can be covered through bilateral contracts, self-production and the pool. Signing bilateral contracts reduces the risk associated with the volatility of pool prices usually incurring higher average prices. Self-producing also reduces the risk related to pool prices. On the other hand, relying mostly on the pool might result in an unacceptable volatility of procurement cost. The problem of defining the right mix among the different sources is complicated by the high uncertainty affecting the parameters involved in the decision process (future market prices, energy demand, self-production from renewable sources). We address this more challenging problem by adopting the stochastic programming framework. The resulting model belongs to the class of two-stage model with recourse. The computational results carried out by considering a real case study shows the validity of the proposed approach.

Hedging Market and Credit Risk in Corporate Bond Portfolios

The European market for corporate bonds has grown significantly over the last two decades to become a preferable financing channel for large corporations in the local and Eurobond markets. The 2008 credit crisis has, however, dramatically changed corporations funding opportunities with similar effects on borrowing policies of sovereigns as well. Accordingly institutional and individual investors have progressively reduced the share of credit risky instruments in their portfolios. This chapter investigates the potential of multistage stochastic programming to provide the desired market and credit risk control for such portfolios over the recent, unprecedented financial turmoil. We consider a Eurobond portfolio, traded in the secondary market, subject to interest and credit risk and analyse whether a jump-to-default risk model and a dynamic control policy would have reduced the impact of severe market shocks on the portfolios during the crisis, to limit the systemic impact of investment strategies. The methodology is shown to provide an effective alternative to popular hedging techniques based on credit derivatives at a time in which such markets became extremely illiquid during the Fall of 2008.