IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-06-16T08:07:42ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2014-03-05T14:26:53Z2014-03-05T14:26:53Zhttp://eprints.imtlucca.it/id/eprint/2178This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/21782014-03-05T14:26:53ZA stochastic optimization approach to optimal bidding on dutch ancillary services marketsThe aim of this paper is to present a market design for trading capacity reserves (also called Ancillary Services, AS) and to introduce a strategy for the optimal bidding problem in such a scenario. In the deregulated market, the presence of several market participants or Balance Responsible Parties (BRPs) entitled for trading energy, together with the increasing integration of renewable sources and price-elastic loads, shift the focus on decentralized control and reliable forecast techniques. The main feature of the considered market design is its double-sided nature. In addition to portfolio-based supply bids and based on prediction of their stochastic production and load, BRPs are allowed to submit risk-limiting requests. Requesting capacity from the AS market corresponds to giving to the market an estimate of the possible deviation from the daily production schedule resulting from the day-ahead auction and from bilateral contracts, named E-Program. In this way each BRP is responsible for the balanced and safe operation of the electric grid. On the other hand, at each Program Time Unit (PTU) BRPs must also offer their available capacity under the form of bids. In this paper, a bidding strategy to the double-sided market is described, where the risk is minimized and all the constraints are fulfilled. The algorithms devised are tested in a simulation environment and compared to the current practice, where the double-sided auction is not contemplated. Results in terms of expected imbalances and reliability are presented.Laura PugliaAlberto Bemporadalberto.bemporad@imtlucca.itAndrej JokicAna Virag2014-03-05T14:18:21Z2014-03-05T14:26:18Zhttp://eprints.imtlucca.it/id/eprint/2177This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/21772014-03-05T14:18:21ZReliability and efficiency for market parties in power systemsIn this paper we present control strategies for solving the problems of risk-averse bidding on the electricity markets, focusing on the Day-Ahead and Ancillary Services market, and of optimal real-time power dispatch from the point of view of a market participant, or Balance Responsible Party (BRP). For what concerns the bidding problem, the proposed algorithms are based on two-stage stochastic programming and are aimed at finding the optimal allocation of production between the day-ahead exchange market and the ancillary services market. For the real-time power dispatch problem, we devised a two-level hierarchical control strategy, where the upper-level computes economically optimal power set-points for the generators, and the lower level tracks them while considering constraints and dynamical models of the plant. Simulation results based on realistic data modeling the Dutch transmission network are shown to evaluate the effectiveness of the approach.Laura PugliaPanagiotis Patrinospanagiotis.patrinos@imtlucca.itDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2012-03-05T11:06:43Z2012-04-04T09:21:01Zhttp://eprints.imtlucca.it/id/eprint/1212This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12122012-03-05T11:06:43ZA multi-stage stochastic optimization approach to optimal bidding on energy marketsOne of the most challenging tasks for an energy producer is represented by the optimal bidding on energy markets. Each eligible plant has to submit bids for the spot market one day before the delivery time and bids for the ancillary services provision. Allocating the optimal amount of energy, jointly minimizing the risk and maximizing profits is not a trivial task, since one has to face several sources of stochasticity, such as the high volatility of energy prices and the uncertainty of the production, due to the deregulation and to the growing importance of renewable sources. In this paper the optimal bidding problem is formulated as a multi-stage optimization problem to be solved in a receding horizon fashion, where at each time step a risk measure is minimized in order to obtain optimal quantities to bid on the day ahead market, while reserving the remaining production to the ancillary market. Simulation results show the optimal bid profile for a trading day, based on stochastic models identified from historical data series from the Italian energy market.Laura PugliaDaniele Bernardinidaniele.bernardini@imtlucca.itAlberto Bemporadalberto.bemporad@imtlucca.it2011-07-29T11:00:09Z2012-07-09T09:42:48Zhttp://eprints.imtlucca.it/id/eprint/745This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7452011-07-29T11:00:09ZA stochastic model predictive control approach to dynamic option hedging with transaction costsThis paper proposes a stochastic model predictive control (SMPC) approach to hedging derivative contracts (such as plain vanilla and exotic options) in the presence of transaction costs. The methodology is based on the minimization of a stochastic measures of the hedging error predicted for the next trading date. Three different measures are proposed to determine the optimal composition of the replicating portfolio. The first measure is a combination of variance and expected value of the hedging error, leading to a quadratic program (QP) to solve at each trading date; the second measure is the conditional value at risk (CVaR), a common index used in finance quantifying the average loss over a subset of worst-case realizations, leading to a linear programming (LP) formulation; the third approach is of min-max type and attempts at minimizing the largest possible hedging error, also leading to a (smaller scale) linear program. The hedging performance obtained by the three different measures is tested and compared in simulation on a European call and a barrier option.Alberto Bemporadalberto.bemporad@imtlucca.itLaura PugliaTommaso Gabbriellini2011-07-29T10:52:50Z2011-08-04T07:29:05Zhttp://eprints.imtlucca.it/id/eprint/739This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7392011-07-29T10:52:50ZScenario-based stochastic model predictive control for dynamic option hedgingFor a rather broad class of financial options, this paper proposes a stochastic model predictive control (SMPC) approach for dynamically hedging a portfolio of underlying assets. By employing an option pricing engine to estimate future realizations of option prices on a finite set of one-step-ahead scenarios, the resulting stochastic optimization problem is easily solved as a least-squares problem at each trading date with as many variables as the number of traded assets and as many constraints as the number of predicted scenarios. After formulating the dynamic hedging problem as a stochastic control problem, we test its ability to replicate the payoff at expiration date for plain vanilla and exotic options. We show not only that relatively small hedging errors are obtained in spite of price realizations, but also that the approach is robust with respect to market modeling errors.Alberto Bemporadalberto.bemporad@imtlucca.itTommaso GabbrielliniLaura PugliaLeonardo Bellucci