Electricity pricing

Summary

Electricity pricing (also referred to as electricity tariffs or the price of electricity) can vary widely by country or by locality within a country. Electricity prices are dependent on many factors, such as the price of power generation, government taxes or subsidies, CO2 taxes, local weather patterns, transmission and distribution infrastructure, and multi-tiered industry regulation. The pricing or tariffs can also differ depending on the customer-base, typically by residential, commercial, and industrial connections. According to the U.S. Energy Information Administration (EIA), "Electricity prices generally reflect the cost to build, finance, maintain, and operate power plants and the electricity grid." Where pricing forecasting is the method by which a generator, a utility company, or a large industrial consumer can predict the wholesale prices of electricity with reasonable accuracy. Due to the complications of electricity generation, the cost to supply electricity varies minu

Official source

https://en.wikipedia.org/wiki/Electricity_pricing

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Forecasting price spikes in European day-ahead electricity markets using decision trees

Anna Fragkioudaki

Although the techniques for normal price prediction are well advanced, price spike prediction remains a challenge. The aim of this paper is to present a method to predict both normal day-ahead electricity prices as well as day-ahead price spikes using classification and regression trees. The models are build using historical data of European electricity market and transmission system variables. The proposed method is tested on several bidding zones of Europe. © 2015 IEEE.

IEEE2015

This paper proposes a pricing scheme for the day-ahead market in power systems with a large percentage of renewable stochastic production. To clear the day-ahead market, instead of a simplistic deterministic model, we use a two-stage stochastic programming model that embodies a prognosis of future operating conditions. Non-convexities due to start-up costs and the on/off status of generators and their minimum power outputs are properly taken into account. Our goal is to obtain uniform day-ahead clearing prices that deviate in the least possible manner from marginal prices and that allow producers to recover their costs without uplifts. The proposed methodology is illustrated using a simple example and a realistic case study.

Ieee-Inst Electrical Electronics Engineers Inc2017

Environomic Optimization of District Heating Network Systems with Both Centralized and Decentralized Heat Pumps.

Vinicio Curti, Daniel Favrat

In Switzerland like in many countries, many of the populated areas are located close to lakes or rivers, offering a large potential for the use of heat pumps. Although district heating is an obvious solution, adapting the delivery temperature to the most exigent users is detrimental to overall system performance. The best system configuration could avoid this pitfall by relying on a centralized plant of heat pumps with cogeneration, supplemented by decentralized heat pumps for the more demanding users. Using expert knowledge to compose a superconfiguration of all possible components for a district heating network system with both centralized and decentralized heat pumps, a novel methodology is proposed for modeling and optimizing, under known sets of economic, user characteristic and environmental constraint data, both the final configuration resulting from the superconfiguration as well as the configuration’s corresponding component designs. The optimization is accomplished with the help of a cutting-edge operations research tool, a genetic algorithm, while the model itself is based on a total cost objective function which unifies in a single model investment, operation, and pollution costs. These latter costs are (for each of the major pollutants) based on the literature as well as on continuous pollution penalty functions adapted to system emissions and to local immissions ratios. These penalties are included to help guide the choice of configuration and component designs away from regions where pollution limits or undesirable levels of actual emissions in combination with local / global conditions are approached too closely. Results are shown for various user distributions and fuel and electricity prices. A comparison between the results found with and without taking into account pollution is also presented.

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