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Forecasting and Risk Management Techniques for Electricity Markets II

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 12806

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Prof. Dr. Yuji Yamada

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Faculty of Business Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo 112-0012, Japan
Interests: electricity market; weather derivatives; financial risk management and hedging; optimization and control
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Dear Colleagues,

The role of forecasting and risk management techniques for electricity markets is becoming increasingly important. Due to the rapid introduction of solar power and other renewable electricity sources, weather and climate changes’ impact on electricity markets in both price and volume executions is growing and the market participants may struggle with uncertainties. Moreover, the system operator (or an aggregator in the region) is required to adjust the imbalance using a backup thermal generation system to match real-time power production with electricity consumption, which results in additional cost or a loss caused by prediction errors. Thermal power is another source of uncertainty in electricity markets, as the cost of generating it largely depends on fuel prices and the type of energy. In such a situation, we need to develop more advanced theories and technologies for supporting risk management in electricity markets, including distributed energy resources (DERs) and peer-to-peer (P2P) energy trading systems.

In this Special Issue, we invite papers exploring theories, applications, simulations, and/or case studies of advanced forecasting and risk management techniques for electricity markets. Topics of interest for publication include (but are not limited to):

Forecasting methods for electricity markets with uncertainty;
Bidding strategies of agents for P2P electricity trading;
Constructions of new derivatives in electricity and energy markets;
Risk-management techniques and strategies using weather and other derivatives;
P2P trading systems and operations with blockchain technologies;
Artificial market simulations of DERs with storage batteries and EV systems;
Advanced technologies for achievement of carbon neutrality.

I look forward to receiving your contributions.

Prof. Dr. Yuji Yamada
Guest Editor

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Keywords

forecasting and risk management techniques
renewable energy
P2P electricity trading and distributed energy resources
new derivatives for energy and electricity trading
weather and other derivatives
carbon neutrality
agent simulations
block-chain technologies

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Forecasting and Risk Management Techniques for Electricity Markets in Energies (10 articles)

Published Papers (9 papers)

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Research

24 pages, 5353 KiB

Open AccessArticle

On the Dynamics of Spot Power Prices across Western Europe in Pandemic Times

by Luis María Abadie and José Manuel Chamorro

Energies 2024, 17(14), 3420; https://doi.org/10.3390/en17143420 - 11 Jul 2024

Viewed by 714

Abstract

Learning the dynamics of power prices in a given market is important for a number of players (e.g., producers, consumers, and policy makers) at both macro- and microeconomic levels. This paper analyzes the recent behavior of spot prices in eight Western European countries. The sample period coincides with the COVID-19 pandemic for the most part: it starts in April 2020 and runs until May 2023; it includes the start of the Russia–Ukraine war. We introduce a new model for the hourly spot price of electricity. The deterministic component includes yearly, weekly, and daily seasonalities; the stochastic component accounts for volatility, mean reversion, and discrete jumps. We estimate the model with publicly available hourly data. Regarding the development of the internal market for electricity, we find that core mainland power markets now move closer in step with one another than before, but the integration process of the Iberian Peninsula seems to have kicked into reverse. As for the dynamics of power prices, in the last part of the sample period the speed of reversion falls everywhere, and price volatility increases noticeably; the expected number of jumps per hour decreases, but their average size turns to positive and they become more volatile. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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16 pages, 2784 KiB

Open AccessArticle

A Voltage-Aware P2P Power Trading System Aimed at Eliminating Unfairness Due to the Interconnection Location

by Satoshi Takayama and Atsushi Ishigame

Energies 2024, 17(4), 841; https://doi.org/10.3390/en17040841 - 10 Feb 2024

Cited by 1 | Viewed by 891

Abstract

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract’s availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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18 pages, 3460 KiB

Open AccessArticle

Credibility Theory-Based Information Gap Decision Theory to Improve Robustness of Electricity Trading under Uncertainties

by Xin Zhao, Peng Wang, Qiushuang Li, Yan Li, Zhifan Liu, Liang Feng and Jiajia Chen

Energies 2023, 16(22), 7543; https://doi.org/10.3390/en16227543 - 12 Nov 2023

Viewed by 862

Abstract

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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24 pages, 3661 KiB

Open AccessArticle

Simulation Analysis of Electricity Demand and Supply in Japanese Communities Focusing on Solar PV, Battery Storage, and Electricity Trading

by Mika Goto, Hiroshi Kitamura, Daishi Sagawa, Taichi Obara and Kenji Tanaka

Energies 2023, 16(13), 5137; https://doi.org/10.3390/en16135137 - 3 Jul 2023

Cited by 2 | Viewed by 1454

Abstract

This study analyzes how the electricity demand and supply constitutions affect electricity independence and power trading within a community and between a community and a grid through simulation analysis. To that aim, we create a simulation model equipped with a community-building function and trading capability. We first construct a community consisting of various types of residential and industrial consumers, and renewable power plants deployed in the community. Residential and industrial consumers are characterized by a state of family/business and ownership and the use of energy equipment such as rooftop solar PV and stationary battery storage in their homes/offices. Consumers’ electricity demand is estimated from regression analyses using training data. Using the hypothetical community constructed for the analysis, the simulation model performs rule-based electricity trading and provides outputs comprising the total electricity demand in the community, the state of use of battery storage and solar PV, the trading volume, and the electricity independence rate of the community. From the simulation results, we discuss policy implications on the effective use of renewable energy and increasing electricity independence by fully utilizing battery and trading functions in a community. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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26 pages, 12052 KiB

Open AccessArticle

Machine Learning-Based Estimation of COP and Multi-Objective Optimization of Operation Strategy for Heat Source Considering Electricity Cost and On-Site Consumption of Renewable Energy

by Daishi Sagawa and Kenji Tanaka

Energies 2023, 16(13), 4893; https://doi.org/10.3390/en16134893 - 23 Jun 2023

Cited by 4 | Viewed by 1268

Abstract

Air conditioning is a significant consumer of electricity in buildings, accounting for around 40% of the total consumption. While previous studies have focused on planning methods to minimize electricity costs, recent years have seen an increasing need for energy management methods that consider environmental performance, such as CO₂ emissions, alongside economic efficiency. This study proposes a mechanism to support stakeholders’ decision-making by calculating Pareto solutions based on the multi-objective optimization of economic and environmental characteristics for entities that own renewable energy generation facilities. Unlike many existing studies that assume a specific equation for COP (Coefficient of Performance) estimation, this study adopts a nonparametric COP estimation method using machine learning, resulting in a more realistic and flexible modeling of the system. The study also presents a model for selecting an operation strategy that balances environmental and economic goals, incorporating a thermal storage facility to improve the renewable energy rate. Specifically, we proposed and compared methods for calculating solutions using only the GA (Genetic Algorithm) and a two-step optimization method combining a GA and gradient-based optimization method, confirming the superiority of the two-step optimization method. The case study unveiled unique operational profiles corresponding to cost-saving, renewable-energy, and balanced orientation points, suggesting the existence of specific strategies tailored to each orientation. The findings of this study can help stakeholders make more informed decisions regarding energy management in air conditioning systems, with benefits for both the environment and the bottom line. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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19 pages, 632 KiB

Open AccessArticle

Capacity Market and Investments in Power Generations: Risk-Averse Decision-Making of Power Producer

by Naoki Makimoto and Ryuta Takashima

Energies 2023, 16(10), 4241; https://doi.org/10.3390/en16104241 - 22 May 2023

Cited by 2 | Viewed by 1235

Abstract

The penetration of power generations from renewable energy sources into the power market has a significant impact on the capacity factor of existing power generations. This is because power producers cannot recover a capital cost of power generations with high operating cost possibly due to underinvestment. One solution to address this problem includes a capacity mechanism; that is, the capacities of the power generations can be sold through a market or a bilateral contract. Many schemes of the capacity mechanism have been used worldwide. In this study, we examine an investment in a power plant in both the electricity and capacity markets. The effect of investment opportunity on uncertainty and risk aversion is analyzed by a real options approach that is one of analytical methods for investment decisions under uncertainty. The investment timing for the standard energy-only market is compared with that for the capacity market. When the risk averse for the power producer is relatively small, the income in the energy-only market is obtained whereas, when the risk averse is relatively high, the income is gained in both the electricity and capacity markets for the sake of enough profit. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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26 pages, 3553 KiB

Open AccessArticle

Construction of Mixed Derivatives Strategy for Wind Power Producers

by Yuji Yamada and Takuji Matsumoto

Energies 2023, 16(9), 3809; https://doi.org/10.3390/en16093809 - 28 Apr 2023

Cited by 4 | Viewed by 1881

Abstract

Due to the inherent uncertainty of wind conditions as well as the price unpredictability in the competitive electricity market, wind power producers are exposed to the risk of concurrent fluctuations in both price and volume. Therefore, it is imperative to develop strategies to effectively stabilize their revenues, or cash flows, when trading wind power output in the electricity market. In light of this context, we present a novel endeavor to construct multivariate derivatives for mitigating the risk of fluctuating cash flows that are associated with trading wind power generation in electricity markets. Our approach involves leveraging nonparametric techniques to identify optimal payoff structures or compute the positions of derivatives with fine granularity, utilizing multiple underlying indexes including spot electricity price, area-wide wind power production index, and local wind conditions. These derivatives, referred to as mixed derivatives, offer advantages in terms of hedge effectiveness and contracting efficiency. Notably, we develop a methodology to enhance the hedge effects by modeling multivariate functions of wind speed and wind direction, incorporating periodicity constraints on wind direction via tensor product spline functions. By conducting an empirical analysis using data from Japan, we elucidate the extent to which the hedge effectiveness is improved by constructing mixed derivatives from various perspectives. Furthermore, we compare the hedge performance between high-granular (hourly) and low-granular (daily) formulations, revealing the advantages of utilizing a high-granular hedging approach. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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25 pages, 6552 KiB

Open AccessArticle

P2P Electricity Trading Considering User Preferences for Renewable Energy and Demand-Side Shifts

by Daishi Sagawa, Kenji Tanaka, Fumiaki Ishida, Hideya Saito, Naoya Takenaga and Kosuke Saegusa

Energies 2023, 16(8), 3525; https://doi.org/10.3390/en16083525 - 18 Apr 2023

Viewed by 1475

Abstract

In the global trend towards decarbonization, peer-to-peer (P2P) energy trading is garnering increasing attention. Furthermore, energy management on the demand side plays a crucial role in decarbonization efforts. The authors have previously developed an automated bidding agent that considers user preferences for renewable energy (RE), assuming users own electric vehicles (EVs). In this study, we expand upon this work by considering users who own not only EVs but also heat pump water heaters, and we develop an automated bidding agent that takes into account their preferences for RE. We propose a method to control the start time and presence of daytime operation shifts for heat pump water heaters, leveraging their daytime operation shift function. Demonstration experiments were conducted to effectively control devices such as EVs and heat pumps using the agent. The results of the experiments revealed that by controlling the daytime operation of heat pumps with our method, the RE utilization rate can be improved compared to scenarios without daytime operation shifts. Furthermore, we developed a simulator to verify the outcomes under different scenarios of demand-side resource ownership rates, demonstrating that higher ownership rates of EVs and heat pumps enable more effective utilization of renewable energy, and that this effect is further enhanced through P2P trading. Based on these findings, we recommend promoting the adoption of demand-side resources such as EVs and heat pumps and encouraging P2P energy trading to maximize the utilization of renewable energy in future energy systems. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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22 pages, 2597 KiB

Open AccessArticle

Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power

by Takuji Matsumoto and Yuji Yamada

Energies 2023, 16(7), 3112; https://doi.org/10.3390/en16073112 - 29 Mar 2023

Cited by 1 | Viewed by 1803

Abstract

Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses. Full article

(This article belongs to the Special Issue Forecasting and Risk Management Techniques for Electricity Markets II)

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