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Showing papers on "Spot contract published in 2022"


Journal ArticleDOI
TL;DR: In this paper , a comparative study of four different approaches to forecast the German day-ahead electricity spot price was conducted, and it was shown that a combination of both forecasting methods outperformed each of the single models.

14 citations



Journal ArticleDOI
TL;DR: In this article, the authors focus on deriving operating rule curves to maximize revenue by integrating reservoir operations and a spot market, and a parameter-simulation-optimization approach is then used to maximize the hydropower revenue and reliability, and minimize spill water simultaneously.

12 citations


Journal ArticleDOI
TL;DR: In this paper , the authors focus on deriving operating rule curves to maximize revenue by integrating reservoir operations and a spot market, and a parameter-simulation-optimization approach is then used to maximize the hydropower revenue and reliability, and minimize spill water simultaneously.

12 citations


Journal ArticleDOI
TL;DR: This article examined the spillovers of informational shocks and the connectedness in China's base metals futures and spot markets, and found that base metals tend to generate more total spillovers in the futures market than in the spot market and that individual metals, apart from nickel, have nearly identical directional spillover impacts on or from other base metals in both the futures and the spot markets.

10 citations


Journal ArticleDOI
TL;DR: In this paper, the spillovers of informational shocks and the connectedness in China's base metals futures and spot markets were examined using the framework of Diebold and Yilmaz (2012, 2014).

10 citations


Journal ArticleDOI
TL;DR: In this paper , a coal-fired coal unit that aims to allocate its capacity to spot, derivative, and bilateral contract markets to maximize its expected profit is presented. But the sensitivity analysis for spot price volatility on the profit is also presented with 20% volatility increase.
Abstract: In deregulated power markets, electricity suppliers have the option to trade in the spot market, derivatives market, and bilateral contract market. The spot market is always available and open to competition, but the variability and risk incurred need to be carefully handled. The suppliers might allocate their capacity in the derivatives and bilateral contract market if these alternatives are more viable. The strike price, bilateral contract price, and spot market prices need to be used to decide the capacity allocation problem considering the generation cost of the supplier. This paper first examines the market design and electricity trading in the Turkish electricity market. Then three problems were proposed for a coal-fired coal unit that aims to allocate its capacity to spot, derivative, and bilateral contract markets to maximize its expected profit. A Monte Carlo method is used for allocated electricity capacities, spot market, strike, and bilateral contract price scenarios. A simulation methodology is then proposed that includes capacities allocated to each market and price scenarios. The best capacity allocation strategy is determined that return the highest expected profits for all market price samples. The model is illustrated for a coal unit in the Turkish electricity market. The results are presented for the case, including 100 spot price samples, 100 capacity scenarios, 3 scenarios for the strike, and bilateral contract prices. The sensitivity analysis for spot price volatility on the profit is also presented with 20% volatility increase. It is shown that allocating the capacity to more than one market can increase the total expected profit for a power supplier and the rate of increase varies depending on the scenario set.

10 citations


Journal ArticleDOI
TL;DR: In this article , the authors investigated the relationship between China's exchange rate, domestic crude oil price, and international crude oil prices using the MS-VAR model, and found that although China's oil price is strongly influenced by the international market, its effect on the international crudeoil price is weak; since the launch of INE crude oil futures in the new regime, the fluctuations in the US dollar against the RMB (USD/CNY) exchange rate has had a significant positive effect on China's crudeoil prices.

10 citations


Journal ArticleDOI
TL;DR: This paper used wavelet coherence analysis on global COVID-19 fear index and soft commodities spot and futures prices to investigate safe-haven properties of soft commodities over the period from January 28, 2020 to April 29, 2021.

9 citations


Journal ArticleDOI
TL;DR: In this paper , the causal relationships among spot and futures prices of crude oil and gold for the Indian market by applying rolling, recursive evolving and asymmetric causality tests, for the period spanning from January 2006 to February 2019, were investigated.

8 citations


Journal ArticleDOI
TL;DR: In this article, the causal relationships among spot and futures prices of crude oil and gold for the Indian market by applying rolling, recursive evolving and asymmetric causality tests, for the period spanning from January 2006 to February 2019, were investigated.

Journal ArticleDOI
TL;DR: In this article , the predictability of the Brent crude oil price was studied and it was shown that simple no-change forecast works better than forecasts based on futures prices over short-term horizons (less than one year).

Journal ArticleDOI
TL;DR: In this paper , a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day was developed to investigate the trading portfolio and risk optimization problem faced by retailers.

Journal ArticleDOI
TL;DR: In this article , the authors analyze the impact of growing renewable energy generation on the instances of negative day-ahead auction prices and conclude that keeping the strong fiscal support for renewables, auction design, and marginal cost bidding in place unchanged would gradually lead to more negative prices in the coming years.


Journal ArticleDOI
TL;DR: In this article , the authors proposed an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market, which consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module.

Journal ArticleDOI
TL;DR: In this article , the authors proposed a portfolio allocation model that defines the optimal composition of call/put options and renewable sources to back a supply contract in a portfolio composed by renewable sources short in supply contracts.

Journal ArticleDOI
TL;DR: In this article , the authors compare an Open Cycle Gas Turbine and a battery as firming options for a hypothetical wind farm in the South Australian region of the NEM, and find OCGT firming capacity helps the wind farm to generate consistent net cash flows, allowing it to withstand the highs and lows of spot market prices whilst covering required contract-fordifference payments associated with hedging.

Journal ArticleDOI
TL;DR: In this article , a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary.
Abstract: With the development of information technology, big data analysis has been highlighted in operations and management. From this viewpoint, this paper studies a buyer's optimal purchasing decisions in combined procurement. For combined procurement, a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary. The optimal purchasing quantity in the long-term contract is examined to maximise the buyer's expected profit from combined procurement. In view of the imperfectness in the spot market, the spot trading liquidity is considered in the buyer's optimal purchasing decision. The properties of the two optimal purchasing quantities are examined and several interesting results are obtained. For example, it is illustrated that a buyer's expected profit may decrease in the spot capacity, a result that has never appeared in the existing literature, which reveals the importance of a buyer's optimal order decision in the presence of spot replenishment. Numerical results and sensitivity analysis are performed to verify the results. Management insights are suggested for a buyer's optimal purchasing decisions in combined procurement with a long-term contract and spot replenishment.

Journal ArticleDOI
07 Apr 2022
TL;DR: In this paper , the authors analyzed electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intradays market prices.
Abstract: The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyze electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 h. Fluctuations are strongly correlated and persistent below 12 h, which contributes to extreme price events and a strong multifractal behavior. On longer time scales, they get anticorrelated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 h. The long-term behavior is strongly influenced by the evolution of a large-scale weather pattern with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of q-Gaussian distributions as the best fit to the empiric distribution of electricity prices.3 MoreReceived 6 December 2021Revised 21 February 2022Accepted 28 February 2022DOI:https://doi.org/10.1103/PRXEnergy.1.013002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasComplex systemsEconophysicsEnergy sourcesFractional Brownian motionPatterns in complex systemsSolar energyStochastic processesWind energyPhysical SystemsFractalsMultiple time scale dynamicsTechniquesNon-Markovian processesTime series analysisStatistical PhysicsInterdisciplinary PhysicsGeneral Physics

Journal ArticleDOI
TL;DR: In this paper , the role of futures markets and their dynamic effects on the stability of commodity prices is investigated based on combining two econometric approaches: a quantile vector autoregression (QVAR) model of the marginal distributions of futures and spot prices, and a copula of their joint distribution.
Abstract: This paper investigates the role of futures markets and their dynamic effects on the stability of commodity prices. The analysis is based on combining two econometric approaches: a quantile vector autoregression (QVAR) model of the marginal distributions of futures and spot prices, and a copula of their joint distribution. Applied to the US soybean and corn markets over the period of 1980–2019, the econometric investigation finds evidence of nonlinear price dynamics that depend on the maturity of the futures contract and documents how marginal price distributions and associated moments evolve over time. Based on the estimates of the QVAR model, we provide evidence of local instability in the upper tail of the price distributions. We find that the futures market helps stabilize the market under nearby futures contract maturity. We document the presence of nonlinear cointegration relationships between futures and spot price. Relying on a copula, we find a positive contemporaneous codependence between futures price and spot price across all quantiles, codependence that varies with the futures contract maturity. We also present evidence of a time-varying basis that affects the convergence properties of the futures and spot price. Our findings shed new light on the joint determination of futures and spot price in commodity markets.

Journal ArticleDOI
TL;DR: In this paper , the authors investigated the pricing efficiency of Shanghai Crude Oil Futures (SC) as a newly emerged crude oil future market from the perspective of cointegration and estimated its contribution to price discovery in China based on the vector error correction model (VECM), permanent-transitory (PT), information share (IS), and modified information share(MIS) models.

Journal ArticleDOI
TL;DR: In this article , the authors investigated the effects of bidding procedures and fairness of generator and consumption sides under three settlement mechanisms: locational marginal pricing, zonal pricing, and average system pricing.

Journal ArticleDOI
TL;DR: In this paper, the optima of a forward contract for hydropower producers to hedge revenue uncertainty sourced from varied spot prices are explored. But, few studies have been explored to jointly derive the optimal optima.
Abstract: Forward contract is a useful tool for hydropower producers to hedge revenue uncertainty sourced from varied spot prices. However, few studies have been explored to jointly derive the optima...

Journal ArticleDOI
TL;DR: In this article , the authors quantified how market power and uncompetitive behavior in forward markets affect equilibrium prices to the detriment of consumers in the electricity industry in Colombia and found that the use of endogenous forward contracts increases spot prices.

Journal ArticleDOI
TL;DR: In this paper , the authors present a survey on the work related to spot instances, by first introducing the development history of spot instance pricing models, then summarizing the methods that can improve the availability of spot instances and finally discussing how to better understand and use spot instances.

Journal ArticleDOI
04 May 2022-Energies
TL;DR: In this paper , the authors analyzed the trading strategy of large retailers in the power market and proved that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market.
Abstract: In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid extreme learning machine (HELM) to forecast the spot market price of electricity in Shanxi, China, based on the trading center information.
Abstract: With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Journal ArticleDOI
TL;DR: In this article , the authors investigated the relationship between prices and demand and found the electricity supply curve is much steeper when demand approaches the capacity limit, suggesting the need to invest more thermal capacity to stabilize spot market prices.

Journal ArticleDOI
16 Jun 2022-Risks
TL;DR: In this paper , the authors used fractionally integrated methods and an Artificial Neural Network (ANN) model to predict commodity prices during the COVID-19 pandemic episode and observed that commodity prices have a mean reverting behavior.
Abstract: Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode.