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[en] Highlights: • Interval based modeling of the profit to describe the goals in a benchmark. • Proposing a new two level profit maximization procedure based on risk management. • Dominance assessment by, Prospect stochastic dominance, Hypothesis testing & CvaR. virtually, all decisions in financial markets are made in the presence of uncertainty. Stochastic dominance is a well-known concept that is broadly implemented for decision making under uncertainty. Thanks to the advantages of this approach, a decision maker is able to exploit available information related to uncertainties with stochastic nature. In this paper, to cope with the uncertainties in the procedure of decision making for forward contracts in an electricity market, a modified stochastic dominance approach is proposed. In the suggested framework, instead of considering a single value for desirable goal in each scenario of benchmark, a profit interval has been allocated to reflect the economic targets of decision maker. In the next step, the allocated profit interval is utilized for decision making problem in the stochastic dominance framework. In order to find the optimal profit profile in such a setting, a two level optimization structure is suggested. To this end, at the lower level of optimization, three different methods namely the prospect stochastic dominance, conditional value at risk (CvaR) and hypothesis testing are applied to find the optimal profit profiles for a number of benchmarks, those are stochastically generated in an interval around a pre-specified profit benchmark. In the upper level, the optimal profit profile is computed by using the Mean-CvaR method. To show the practical aspects and generalizability of our proposed approach, the methods are applied to two different cases, including a retailer and an electricity producer’s decision making problems to determine their involvement in the futures market by maximizing their expected profit over a given planning horizon, while controlling the risk of profit volatility in the electricity market, raised from the uncertainties in the spot market price and the consumer demand. The performance of suggested framework is evaluated through simulation results and relevant conclusions are drawn.
[en] In an electricity market, the retailer sets up contracts with the wholesale side for purchasing electricity and with the customers for its selling. This paper proposes a mathematical method based on mixed-integer stochastic programming to determine the optimal sale price of electricity to customers and the electricity procurement policy of a retailer for a specified period. The retailer has multiple choices for electricity procurement, such as spot market, forward contracts, call options and self-production. Risk is considered and modeled by conditional value-at-risk methodology. Also, the competition between retailers is modeled using a market share function. A case study is illustrated to demonstrate the capability of the proposed method. (author)
[en] In a competitive market scenario, consumers make payments for the consumption of electricity to retailers at fixed tariff. The retailers buy power at the Market Clearing Price (MCP) in spot market and/or through bilateral contract at agreed upon price. Due to these different modes at buying and selling ends, the retailers are faced with an involved task of estimating their payoffs along with the risk-quantification. The methodology presented in this paper gives a range of bilateral quantity and associated price for a retailer to ensure risk-constrained payoff. The exercise is carried out with a single retailer in the market as well as for a case of competition amongst two retailers. Risk is quantified using Risk Adjusted Recovery on Capital (RAROC). The problem is evaluated to get a range of bilateral quantity to be quoted for a particular bilateral price at fixed tariff of loyal load and fixed value of switching load. This summary combined with risk-averseness of the retailer leads him to make a judicial choice about bilateral transactions such that it leads to a risk-constrained payoff. (author)
[en] This paper studies asymmetric price responses of individual firms, via daily retail prices of almost all gasoline stations in the Netherlands and suggested prices of the five largest oil companies over more than two years. I find that 38% of the stations respond asymmetrically to changes in the spot market price. Hence, asymmetric pricing is not a feature of the market as a whole, but of individual firms. For asymmetrically pricing stations, the asymmetry is substantial directly after a change but disappears after one or two days. I study station-specific characteristics and conclude that asymmetric pricing seems to be a phenomenon that is randomly distributed across stations. I also find that none of the five largest oil companies adjust their suggested prices asymmetrically.
[en] This study analyses original panel data from 86 countries between 1985 and 2006. Econometric methods were used to identify the effects of different policy devices of power sector reforms on performance indicators (installed capacity per capita, transmission and distribution loss) in the countries analyzed. The research findings suggest that reform variables such as the entry of independent power producers (IPPs), unbundling of generation and transmission, establishment of regulatory agencies, and the introduction of a wholesale spot market are the driving forces of increasing generation capacity, as well as reducing transmission and distribution loss in the respective regions. In this study, we can assume that, firstly, different electric industry's reform policies/measures have different impacts on geographically and economically diverse countries. Secondly, a country's state of economic development has a different impact on policy effects of reforms. Thirdly, coexistent with independent regulatory agencies, reform policy becomes more powerful in realizing sector performances. (author)
[en] As market intermediaries, electricity retailers buy electricity from the wholesale market or self-generate for re(sale) on the retail market. Electricity retailers are uncertain about how much electricity their residential customers will use at any time of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, retailers generally rely on storage to manage demand uncertainty. On electricity markets, retailers are exposed to joint quantity and price risk on an hourly basis given the physical singularity of electricity as a commodity. In the literature on electricity markets, few articles deals on intra-day hedging portfolios to manage joint price and quantity risk whereas electricity markets are hourly markets. The contributions of the article are twofold. First, we define through a VaR and CVaR model optimal portfolios for specific hours (3 a.m., 6 a.m.,...,12 p.m.) based on electricity market data from 2001 to 2011 for the French market. We prove that the optimal hedging strategy differs depending on the cluster hour. Secondly, we demonstrate the significantly superior efficiency of intra-day hedging portfolios over daily (therefore weekly and yearly) portfolios. Over a decade (2001-2011), our results clearly show that the losses of an optimal daily portfolio are at least nine times higher than the losses of optimal intra-day portfolios. (authors)
[en] Industry structure: Over the last few years or so there has been a growing trend towards vertical reintegration between generators and retailers in the Australian electricity industry. By 2013, the three largest power companies (Origin Energy, AGL Energy and EnergyAustralia) jointly supplied over 75% of small electricity retail customers and controlled about 36% of the generation capacity in the NEM region. Vertical reintegration enables these companies to internally manage the risk of price volatility in the spot market and at the same time poses a potential barrier to entry and expansion for generators and retailers that are not vertically integrated. Since 2009, around 45% of the new generation capacities commissioned or committed in the NEM regions is owned by these three large companies. In contrast, investments in generation by entities that are not integrated have been negligible in the same period. Such reintegration has further reduced the scope for new market participants to become active in the energy futures markets to manage risks and secure future earnings. By 2011, low liquidity in the energy futures markets was observed, especially in South Australia where the electricity industry has the highest degree of vertical integration. Such a low liquidity has created a challenging operating environment that, it is argued, is likely to deter efficient investments by new entrants
[en] This paper analyzes the potential influence in Chinese electricity market due to the reform and access of the electricity spot market. On this occasion, a deep learning based model for load forecasting is proposed to improve the market operator’s precise scheduling level and assist power retailers in managing bid strategies. Long-Short Term Memory (LSTM) unit is used to modeling, which is one of the most popular techniques of deep learning. In addition, historical power load data and meteorological data of Suzhou and Lianyungang in China from January 2015 to December 2017 are used for the study to training and evaluate forecasting model. As a result, this paper shows the compare results with exiting machine algorithm for load forecasting. (paper)
[en] The theoretical framework developed in this study allows development of a model of deregulated electricity markets that explains two familiar empirical findings; the existence of forward premiums and price-cost markups in the spot market. This is a significant contribution because electricity forward premiums have been previously explained exclusively by the assumptions of perfect competition and risk-averse behavior while spot markups are generally the outcome of a body of literature assuming oligopolistic competition. Our theoretical framework indicates that a certain premium for forward contracting is required for efficient allocation of generation capacity. However, due to the uniqueness of electricity and the design of deregulated electricity markets this premium might be substantially higher than its optimal level. - Research highlights: → The state of knowledge regarding modeling electricity markets is incomplete. → Electricity forward premiums are not necessarily driven by risk aversion. → Efficiency in production requires a certain premium for forward contracting. → It is likely that market premiums are substantially higher than their optimal level. → Policy regulation should not seek to eliminate forward premium entirely.