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Register a new userMinimising the expectation value of the procurement cost in electricity markets based on the prediction error of energy consumption
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In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {it day-ahead} and {it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in two markets are known. The expectation value of the total electricity cost is minimised over two parameters that change the amounts of electricity. Two parameters depend only on the expected unit prices of electricity and the distributions of prediction errors for the electricity demand traded in two markets. That is, even if we do not know the predictions for the electricity demand, we can determine the values of two parameters that minimise the expectation value of the procurement cost of electricity in two popular spot markets. We demonstrate numerically that the estimate of two parameters often results in a small variance of the total electricity cost, and illustrate the usefulness of the proposed procurement method through the analysis of actual data.
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This paper outlines a critical gap in the assessment methodology used to estimate the macroeconomic costs and benefits of climate policy. It shows that the vast majority of models used for assessing climate policy use assumptions about the financial system that sit at odds with the observed reality. In particular, the models assumptions lead to `crowding out of capital, which cause them to show negative impacts from climate policy in virtually all cases. We compare this approach with that of the E3ME model, which follows non-equilibrium economic theory and adopts a more empirical approach. While the non-equilibrium model also has limitations, its treatment of the financial system is more consistent with reality and it shows that green investment need not crowd out investment in other parts of the economy -- and may therefore offer an economic stimulus. The implication of this finding is that standard CGE models consistently over-estimate the costs of climate policy in terms of GDP and welfare, potentially by a substantial amount. These findings overly restrict the range of possible emission pathways accessible using climate policy from the viewpoint of the decision-maker, and may also lead to misleading information used for policy making. Improvements in both modelling approaches should be sought with some urgency -- both to provide a better assessment of potential climate policy and to improve understanding of the dynamics of the global financial system more generally.
Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer thereby representing the underlying physics of the electricity system, in contrast to the traditional input-output models of carbon accounting. With this method we explore the hourly structure of electricity trade across Europe in 2017, and find substantial differences between production and consumption intensities. This emphasizes the importance of considering cross-border flows for increased transparency regarding carbon emission accounting of electricity.
Inside the EU, the commercial integration of the CEE countries has gained remarkable momentum before the crisis appearance, but it has slightly slowed down afterwards. Consequently, the interest in identifying the factors supporting the commercial integration process is high. Recent findings in the new trade theory suggest that FDI influence the trade intensity but the studies approaching this relationship for the CEE countries present mixed evidence, and investigate the commercial integration of CEE countries with the old EU members. Against this background, the purpose of this paper is to assess the CEE countries intra-integration, focusing on the Czech Republic, Hungary, Poland and the Slovak Republic. For each country we employ a panel gravitational model for the bilateral trade and FDI, considering its interactions with the other three countries in the sample on the one hand, and with the three EU main commercial partners on the other hand. We investigate different facets of the trade -- FDI nexus, resorting to a fixed effects model, a random effects model, as well as to an instrumental variable estimator, over the period 2000-2013. Our results suggest that outward FDI sustains the CEE countries commercial integration, while inward FDI has no significant effect. In all the cases a complementarity effect between trade and FDI is documented, which is stronger for the CEE countries historical trade partners. Consequently, these findings show that CEE countries policymakers are interested in encouraging the outward FDI toward their neighbour countries in order to increase the commercial integration.
When participating in electricity markets, owners of battery energy storage systems must bid in such a way that their revenues will at least cover their true cost of operation. Since cycle aging of battery cells represents a substantial part of this operating cost, the cost of battery degradation must be factored in these bids. However, existing models of battery degradation either do not fit market clearing software or do not reflect the actual battery aging mechanism. In this paper we model battery cycle aging using a piecewise linear cost function, an approach that provides a close approximation of the cycle aging mechanism of electrochemical batteries and can be incorporated easily into existing market dispatch programs. By defining the marginal aging cost of each battery cycle, we can assess the actual operating profitability of batteries. A case study demonstrates the effectiveness of the proposed model in maximizing the operating profit of a battery energy storage system taking part in the ISO New England energy and reserve markets.
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