Forecasting and Risk Management Techniques for Electricity Markets

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

Financing Sustainable Energy Transition with Algorithmic Energy Tokens

Financing energy firms and catalyzing the energy transition are pivotal for achieving a sustainable future. In this era of increasing environmental consciousness, banks are incorporating environmental considerations into their credit rating methodologies, like the Partnership for Carbon Accounting Financial Guidelines. In the meantime, the advent of digital tokens offers new avenues for energy token creation. This study establishes a factor model as the fundamental framework for algorithmic energy tokens and employs gradient-boosting tree regression to examine energy price drivers in Italy and Austria. The results underscore the heightened motivation to invest in energy transition and security during periods of elevated energy prices. Conversely, the drive to invest in clean energy sources diminishes when operational profits are low or energy security must be maintained. This research elucidates on an innovative financing solution that handles these dynamics, produces momentum, and focuses special emphasis on its potential for implementing environmental policies by developing an algorithmic energy token mechanism based on environmental regulations and considerations

Design of strategies for the implementation and management of a complementary monetary system using the SWOT-AHP methodology

[EN] The objective of this research is to contribute to the scientific debate on “complementary monetary systems” (CMSs), what strategies may be the best for allowing the implementation of a CMS in a territory and that optimise the potential that it seems to have to strengthen processes of sustainable local development and urban resilience. For this, the Strengths, Weaknesses, Opportunities and Threats-Analytic Hierarchy Process methodology (SWOT-AHP) has been used, which has allowed us to identify four strategies: (1) build a social, economic and political consensus, (2) create a community observatory for “complementary social monetary systems” (CSMSs), (3) define communication tools for raising awareness and education in ethical finance and (4) promote the alignment of the CSMS with sustainable local development strategies. These strategies have been formulated so that that they can be implemented by any entity, public or private, and for any of the types of CMS that may be part of a CSMS.S

Design of Strategies for the Implementation and Management of a Complementary Monetary System Using the SWOT-AHP Methodology

The objective of this research is to contribute to the scientific debate on “complementary monetary systems” (CMSs), what strategies may be the best for allowing the implementation of a CMS in a territory and that optimise the potential that it seems to have to strengthen processes of sustainable local development and urban resilience. For this, the Strengths, Weaknesses, Opportunities and Threats-Analytic Hierarchy Process methodology (SWOT-AHP) has been used, which has allowed us to identify four strategies: (1) build a social, economic and political consensus, (2) create a community observatory for “complementary social monetary systems” (CSMSs), (3) define communication tools for raising awareness and education in ethical finance and (4) promote the alignment of the CSMS with sustainable local development strategies. These strategies have been formulated so that that they can be implemented by any entity, public or private, and for any of the types of CMS that may be part of a CSMS

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%