Forecasting and Risk Management Techniques for Electricity Markets

doi:10.3390/books978-3-0365-5184-5

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Forecasting and Risk Management Techniques for Electricity Markets

Edited by

September 2022

212 pages

ISBN978-3-0365-5183-8 (Hardback)
ISBN978-3-0365-5184-5 (PDF)

https://doi.org/10.3390/books978-3-0365-5184-5

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This book is a reprint of the Special Issue Forecasting and Risk Management Techniques for Electricity Markets that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents.

The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading.

The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects.

Format

Hardback

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Keywords

electricity markets; non-parametric regression; minimum variance hedge; spline basis functions; cyclic cubic spline; weather derivatives; n/a; distributed energy resources (DER); P2P energy trading; cooperative mechanism; renewable energy; multi agent system; blockchain; cashflow management of electricity businesses; electricity derivatives and forwards; retailers and power producers; solar power and thermal energy; optimal hedging using nonparametric techniques; empirical simulations; peer-to-peer energy trading; distributed energy resources; microgrid; blockchain; digital grid; bidding strategy; electricity price; electricity load; electricity price forecasting; wind energy; day-ahead market; intra-day market; balancing power market; blockchain; peer to peer energy market; hardware control; demonstration experiment; home energy management systems; electric vehicles; P2P energy trading; bidding agent; electric vehicle; functional autoregressive model; functional principle component analysis; vector autoregressive model; functional final prediction error (FFPE); naive method; P2P electricity market; market maker; liquidity; price fluctuation; bidding strategy; artificial market simulation