scholarly journals Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 596-632
Author(s):  
Stephen Haben ◽  
Julien Caudron ◽  
Jake Verma

The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.

2020 ◽  
Author(s):  
Carlo Schmitt ◽  
Kenneth Samaan ◽  
Henrik Schwaeppe ◽  
Albert Moser

The energy system decarbonization and decentralization<br>require coordination schemes for distributed generators<br>and flexibilities. One coordination approach is local energy markets for trading energy among local producers and consumers. The resulting local coordination leads to the questions of how the interaction between local and wholesale markets will be designed and of how the introduction of local energy markets influences the wholesale market system. Therefore, this paper proposes a bottom-up modeling method for local markets within a pan- European wholesale market model. Furthermore, an aggregation-disaggregation method for local markets is developed to reduce computational effort. A case study for local markets in Germany shows the computational advantages of the aggregation-disaggregation method. Preliminary results indicate the impact of different interaction designs between local and wholesale markets on the wholesale market and show the need for further research.


2020 ◽  
Author(s):  
Carlo Schmitt ◽  
Kenneth Samaan ◽  
Henrik Schwaeppe ◽  
Albert Moser

The energy system decarbonization and decentralization<br>require coordination schemes for distributed generators<br>and flexibilities. One coordination approach is local energy markets for trading energy among local producers and consumers. The resulting local coordination leads to the questions of how the interaction between local and wholesale markets will be designed and of how the introduction of local energy markets influences the wholesale market system. Therefore, this paper proposes a bottom-up modeling method for local markets within a pan- European wholesale market model. Furthermore, an aggregation-disaggregation method for local markets is developed to reduce computational effort. A case study for local markets in Germany shows the computational advantages of the aggregation-disaggregation method. Preliminary results indicate the impact of different interaction designs between local and wholesale markets on the wholesale market and show the need for further research.


2019 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Ketut Sukiyono ◽  
Miftahul Janah

Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).


Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 92
Author(s):  
Ioannis P. Panapakidis ◽  
Nikolaos Koltsaklis ◽  
Georgios C. Christoforidis

In contemporary energy markets, the Retailer acts as the intermediate between the generation and demand sectors. The scope of the Retailer is to maximize its profits by selecting the appropriate procurement mechanism and selling price to the consumers. The wholesale market operation influences the profits since the mix of generation plants determines the system marginal price (SMP). In the related literature, the SMP is treated as a stochastic variable, and the wholesale market conditions are not taken into account. The present paper presents a novel methodology that aims at connecting the wholesale and retail market operations from a Retailer’s perspective. A wholesale market clearing problem is formulated and solved. The scope is to examine how different photovoltaics (PV) penetration levels in the generation side influences the profits of the Retailer and the selling prices to the consumers. The resulting SMPs are used as inputs in a retailer profit maximization problem. This approach allows the Retailer to minimize economic risks and maximize profits. The results indicate that different PV implementation levels on the generation side highly influences the profits and the selling prices.


2021 ◽  
Vol 299 ◽  
pp. 117249
Author(s):  
Wilhelm Cramer ◽  
Klemens Schumann ◽  
Michael Andres ◽  
Chris Vertgewall ◽  
Antonello Monti ◽  
...  

2019 ◽  
Vol 6 (5) ◽  
pp. 168
Author(s):  
M.B. Dastagiri ◽  
L. Bhavigna

Agricultural prices play greater role in living Economics. Since many decades’ farmers faced declining agricultural prices and low prices in developing countries. Therefore, in these countries agricultural price policies are under closer appraisal.  Government and policy makers worry about inflation. Economic precision is required in determining prices. This understanding led to conception of the study. The specific objectives are to review various agricultural price theories, research evidences and construct the theory of agricultural price bubble and crash and their effect on macro economy and suggest measures to improve. The study reviews various agricultural price theories, concepts, policies, research gaps and do meta-analysis and formulated the theory of Agricultural prices bubble and price crash. Since 1950, many development economists and practitioners prophesy in developing countries is that low agricultural commodities prices discourage poverty alleviation. Many countries are unable to make successful pricing policies due to there is not enough operative methodological and theoretical support for decision-making. According to the economic theory of cooperativism, the entities come closer to the pecking order theory. Unexpected changes and changes in regulations can have significant impact on the profitability of farming activities. “Demand channel" is the crucial factor in elucidation of commodity price growth. Future prices moments in agriculture have fat-tailed distributions and display quick and unpredicted price jumps. World Trade Organization study highlights the importance of strengthening multilateral disciplines on both import and export trade interventions to food price fluctuations to reduce beggar-thy-neighbor unilateral trade policy. The theory of NAFTA regionalism did not lead to regionalization and not increasing share of intraregional international trade. In EU countries land rents in modern agriculture causing upward trend in agricultural land prices. Information friction, agricultural supports, agricultural price & trade policies, agricultural price transmission are responsible price fluctuations. In economic theory, asymmetric price transmission has been the subject of considerable attention in agricultural gaps. Selection of forecasting models are based on chaos theory. Chaos in agricultural wholesale price data provides a good theoretical basis for selecting forecasting models. This theory can be applied to agricultural prices forecasting. Novelties in agricultural products fluctuations research offer scientific basis in planning of agricultural production.


2021 ◽  
Vol 281 ◽  
pp. 115963 ◽  
Author(s):  
Konstantinos Christidis ◽  
Dimitrios Sikeridis ◽  
Yun Wang ◽  
Michael Devetsikiotis
Keyword(s):  

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