• Energy Research

This paper presents a bi-objective model for optimizing pig deliveries to the abattoir accounting for total revenue and CO 2 emissions. Fattening farms house the most important stage in pig production, and operations on farms must be coordinated with the rest of the pig supply chain when batch management is generally applied. The novelty of the model lies in the change of attitude in producers towards a greener production, which is becoming one of the major concerns in our society. In this context, we enrich the classical approach focused on revenues with the addition of the CO 2 emissions from the pigs on the fattening farms. Emissions derived from feeding and transportation are considered since they are the most important sources of CO 2 . The model is tested using parameters representing a typical integrated Spanish fattening farm. Our findings reveal the impact and the relationship between revenues and emissions, highlight that the break-even is reached achieving 459 kg of CO 2 per pig, which corresponds to a reduction of 6.05%. On the other hand, the profit is slightly reduced by 4.48% in favor of the environment.

Carbon capture and sequestration is a possible technology for abating carbon dioxide emissions. This is costly and requires investment in capture, transportation and storage facilities, and compensation for possibly substantial operational cost at these facilities. On the other hand, this option avoids buying carbon offsets, and the CO2 may in some cases be used for enhanced oil recovery. Stochastic dynamic programming is applied to perform the underlying investment analysis, that is, to decide whether investment on a CO2 value chain is profitable, and if so, then when the decisions should be taken. The oil and CO2 prices are modelled as stochastic processes. As a case study we consider possible CO2 value chain investments on the Norwegian Continental Shelf.

In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account the action of the competing generation companies and the market-price formation rules and must be coordinated with long-term planning results. This paper presents a three stage optimization process with a data analysis and parameter calculation, a linearized unit commitment, and a nonlinear generation scheduling refinement. Although the procedure has been developed from the experience with the Spanish power market, with minor adaptations it is also applicable to any generation company participating in a competitive market system.

Many of the existing electricity markets are of the mixed type, which has pool auction and bilateral contracts between producers and distributors. In this case, the problem faced by a Generation Company (GenCo) is that of maximizing the revenues from participating in the market through the pool auction while honoring the bilateral contracts agreed, for which the revenue is fixed. The extension to mixed markets of a medium-term model, successfully employed for auction-only markets, is presented. It results in a non-convex expected revenue function to be maximized subject to constraints, for which the currently available direct global-optimization solvers prove not to be efficient enough. A heuristic procedure based on a sequence of solutions by a nonlinear solver is presented, and numerical results obtained with several realistic cases show satisfactory results. The test cases presented have dispatchable and non-dispatchable renewables and consider medium-term pumping together with conventional units by all GenCos participating in the mixed market. The advantages for GenCos of employingmedium-termresults as those produced by themodel presented, include, among others, the evaluation of the expected profitability of their bilateral contracts. L. Marí and N. Nabona were supported by project DPI2008-02153,A. Pagès was supported by projects DPI2002- 03330 and DPI2005-09117-C02-01 of Ministerio de Ciencia e Innovaci´on (Spain), project TRA2013-48180-C3-P of the Spanish Ministry of Economy and Competitiveness and the CYTED Program (CYTED2014-515RT0489).

Abstract This paper presents a mathematical model for designing a carbon dioxide (CO 2 ) value chain. Storage of CO 2 in geological formations is recognized as an important alternative for carbon abatement. When CO 2 is deposited in oil reservoirs it can sometimes be used to achieve additional oil production, enhanced oil recovery (EOR). The model determines an optimal CO 2 value chain from a fixed set of CO 2 emission points and a set of potential injection sites. It designs a transport network and chooses the best suited oil fields with EOR potential or other geological formations for storage. A net present value criterion is used. The model is illustrated by an example of a Norwegian case with 14 oil fields, two aquifers and five CO 2 sources. A sensitivity analysis is performed on the most important parameters.

We address the problem of finding optimal medium- term generation policies for a specific generation company by modeling the supply side of a liberalized electricity market. The model assumes a noncooperative oligopoly and determines the joint optimal generation policies of all market generators, taking into account hydro, market, and system uncertainties. We propose an endogenous function of market price with respect to load duration where the choice of fuel and technology influences both the average and range of variation of the medium-term market price. We assume an inelastic demand represented by the load-duration curve, which is matched using the Bloom and Gallant formulation. This accounts for unit outages without using scenarios, which are reserved for modeling other uncertainties such as a latent price variable and the hydro inflows. The equilibrium is solved using the Nikaido-Isoda relaxation algorithm, which enables a series of multistage cubic stochastic programming models to be solved. In order to deal with the large number of load matching constraints, we use a heuristic which allows us to generate only those constraints that will presumably be active at the optimal solution. The model is calibrated to the Spanish electricity market using historical price and generation data.