Essays on the Interaction Between Risk and Market Structure in Electricity Markets

<p>This thesis proposes a new framework to jointly analyze electricity spot market and hedging decisions in an oligopolistic setup. Firstly, we find that, when exogenous, both quantity of electricity hedged by contract and vertical integration decrease the equilibrium spot price. Secondly, we use a hybrid approach and show that market structure can affect a generator’s decision to vertically integrate under uncertain demand. Thirdly, we consider uncertainty in costs and demand and show that concentration in the spot market, for a given hedge quantum, can increase forward prices and affect the slope of the forward curve. Our empirical results indicate that the model fits the New Zealand electricity market well. This evidence that market structure and hedging decisions are closely connected is further explored in a three period equilibrium model for the spot and forward markets, where hedging occurs prior to the submission of supply curves. Taking into account demand-side and supply-side uncertainties, we find that when hedging is endogenous, hedging quantities are affected by spot market parameters, but market power is itself mitigated in the conscious hedging choice of generators. We also show that forward markets can coexist with highly vertically integrated markets. The importance of our results is general. Our models can be used by policy makers to analyze investment and forward price implications of changes in the spot market structure. Our results also indicate that electricity generators, in equilibrium, face a trade-off between market power and hedging. Given that it is socially beneficial to manage risk, the equilibrium impact of their choices on welfare should not be considered in isolation by competition authorities.</p>

Essays on the Interaction Between Risk and Market Structure in Electricity Markets

<p>This thesis proposes a new framework to jointly analyze electricity spot market and hedging decisions in an oligopolistic setup. Firstly, we find that, when exogenous, both quantity of electricity hedged by contract and vertical integration decrease the equilibrium spot price. Secondly, we use a hybrid approach and show that market structure can affect a generator’s decision to vertically integrate under uncertain demand. Thirdly, we consider uncertainty in costs and demand and show that concentration in the spot market, for a given hedge quantum, can increase forward prices and affect the slope of the forward curve. Our empirical results indicate that the model fits the New Zealand electricity market well. This evidence that market structure and hedging decisions are closely connected is further explored in a three period equilibrium model for the spot and forward markets, where hedging occurs prior to the submission of supply curves. Taking into account demand-side and supply-side uncertainties, we find that when hedging is endogenous, hedging quantities are affected by spot market parameters, but market power is itself mitigated in the conscious hedging choice of generators. We also show that forward markets can coexist with highly vertically integrated markets. The importance of our results is general. Our models can be used by policy makers to analyze investment and forward price implications of changes in the spot market structure. Our results also indicate that electricity generators, in equilibrium, face a trade-off between market power and hedging. Given that it is socially beneficial to manage risk, the equilibrium impact of their choices on welfare should not be considered in isolation by competition authorities.</p>

Socio-Economic Factors Influencing Usage Ofclimate Driven Seed Maize in Zimbabwe

The study sought to analyze the factors influencing the use of climate specific certified seeds by maize farmers in Zimbabwe. In terms of the specific objectives the study determined the level of certified seeds produced and utilized in Zimbabwe and examined the factors that influence the level of climate specific certified seeds by farmers in Zimbabwe (2010-2016) farming seasons. In terms of the research methodology, the study adopted an explanatory and gathered data from 53 participants within the maize value chain using a questionnaire. Drawing from the findings, the study revealed that the certified seed maize production increased significantly although utilization declined in the same 2010-2016 period and the surplus gap between supply and demand of certified seed was exported due to low local utilization level. The research concluded that 11.38% of the variance in certified seed maize utilized by farmers under unions are explained by the price of hybrid seed maize, quantities of fertilizer, seed maize, herbicides produced by agricultural companies as well as age differences, educational differences, backward and forward markets within the maize value chain. In order to address the gap between seedmaize produced and adopted by farmer unions, the government, must develop exist strategies which involves rural financing and lending options and prioritize a culture of investing innovations among farmer unions. Such investment strategies must provide access to climate specific certified seeds such as high analysis maize blend fertilizers, hybrid seeds, herbicides and irrigation equipment required as a package to improve maize production in Zimbabwe.

Renewable Power and Electricity Prices: The Impact of Forward Markets

Increasing variable renewable power generation (e.g., wind) is expected to reduce wholesale electricity prices by virtue of its low marginal production cost. This merit-order effect of renewables displacing incumbent conventional (e.g., gas) generation forms the theoretical underpinning for investment decisions and policy in the power industry. This paper uses a game-theoretic market model to investigate how intermittently available wind generation affects electricity prices in the presence of forward markets, which are widely used by power companies to hedge against revenue variability ahead of near-real-time spot trading. We find that in addition to the established merit-order effect, renewable generation affects power prices through forward-market hedging. This forward effect reinforces the merit-order effect in reducing prices for moderate amounts of wind generation capacity but mitigates or even reverses it for higher capacities. For moderate wind capacity, uncertainty over its output increases hedging, and these higher forward sales lead to lower prices. For higher capacities, however, wind variability conversely causes power producers to behave less aggressively in forward trading for fear of unfavorable spot-market positions. The lower sales counteract the merit-order effect, and prices may then paradoxically increase with wind capacity despite its lower production cost. We confirm the potential for such reversals in a numerical study, suggesting new empirical questions while providing potential explanations for previously contradictory observed effects of market fundamentals. We conclude that considering the conventional merit-order effect alone is insufficient for evaluating the price impacts of variable renewable generation in the presence of forward markets. This paper was accepted by Vishal Gaur, operations management.

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

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.