Do you want to publish a course? Click here

Determining Fundamental Supply and Demand Curves in a Wholesale Electricity Market

236   0   0.0 ( 0 )
 Added by Sergei Kulakov
 Publication date 2019
  fields Economy Financial
and research's language is English




Ask ChatGPT about the research

In this paper we develop a novel method of wholesale electricity market modeling. Our optimization-based model decomposes wholesale supply and demand curves into buy and sell orders of individual market participants. In doing so, the model detects and removes arbitrage orders. As a result, we construct an innovative fundamental model of a wholesale electricity market. First, our fundamental demand curve has a unique composition. The demand curve lies in between the wholesale demand curve and a perfectly inelastic demand curve. Second, our fundamental supply and demand curves contain only actual (i.e. non-arbitrage) transactions with physical assets on buy and sell sides. Third, these transactions are designated to one of the three groups of wholesale electricity market participants: retailers, suppliers, or utility companies. To evaluate the performance of our model, we use the German wholesale market data. Our fundamental model yields a more precise approximation of the actual load values than a model with perfectly inelastic demand. Moreover, we conduct a study of wholesale demand elasticities. The obtained conclusions regarding wholesale demand elasticity are consistent with the existing academic literature.



rate research

Read More

Economic shocks due to Covid-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of Covid-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the models dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful.
We provide quantitative predictions of first order supply and demand shocks for the U.S. economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 22% of the US economys GDP, jeopardise 24% of jobs and reduce total wage income by 17%. At the industry level, sectors such as transport are likely to have output constrained by demand shocks, while sectors relating to manufacturing, mining and services are more likely to be constrained by supply shocks. Entertainment, restaurants and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks -- we expect them to be substantially amplified by feedback effects in the production network.
Natural and anthropogenic disasters frequently affect both the supply and demand side of an economy. A striking recent example is the Covid-19 pandemic which has created severe disruptions to economic output in most countries. These direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation. We find that even in this best case scenario network effects substantially amplify the initial shocks. To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on industries if they are not able to satisfy incoming demand. Our results show that economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions.
The authors provide a comprehensive overview of flexibility characterization along the dimensions of time, spatiality, resource, and risk in power systems. These dimensions are discussed in relation to flexibility assets, products, and services, as well as new and existing flexibility market designs. The authors argue that flexibility should be evaluated based on the dimensions under discussion. Flexibility products and services can increase the efficiency of power systems and markets if flexibility assets and related services are taken into consideration and used along the time, geography, technology, and risk dimensions. Although it is possible to evaluate flexibility in existing market designs, a local flexibility market may be needed to exploit the value of the flexibility, depending on the dimensions of the flexibility products and services. To locate flexibility in power grids and prevent incorrect valuations, the authors also discuss TSO-DSO coordination along the four dimensions, and they present interrelations between flexibility dimensions, products, services, and related market designs for productive usage of flexible electricity.
We attempt to reconcile Gabaix and Koijens (GK) recent Inelastic Market Hypothesis with the order-driven view of markets that emerged within the microstructure literature in the past 20 years. We review the most salient empirical facts and arguments that give credence to the idea that market price fluctuations are mostly due to order flow, whether informed or non-informed. We show that the Latent Liquidity Theory of price impact makes a precise prediction for GKs multiplier $M$, which measures by how many dollars, on average, the market value of a company goes up if one buys one dollar worth of its stocks. Our central result is that $M$ increases with the volatility of the stock and decreases with the fraction of the market cap. that is traded daily. We discuss several empirical results suggesting that the lions share of volatility is due to trading activity.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا