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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: closed (20 April 2022) | Viewed by 43048

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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,

Electricity market participants are exposed to many sources of risk. For example, due to the rapid introduction of solar power and other renewable electricity generation, there is a growing impact of weather and climate changes on electricity markets for both price and volume executions. On the other hand, the system operator (or an aggregator in the region) needs to adjust the imbalance using a backup thermal generation system to match real-time power production with demand, which varies with, e.g., temperature, humidity, and other conditions. This requirement leads to an additional cost or a loss for both/either consumers and/or power producers in the network. The use of thermal power also provides another source of uncertainty in electricity markets, as the generation cost largely depends on fuel prices and type of energy. Under these circumstances, forecasting, and risk management techniques have become one of the most important issues for traditional electricity markets as well as recently developed peer-to-peer (P2P) trading systems.

In this Special Issue, we invite papers exploring solar power and demand forecasting, trading and hedging strategies, risk management techniques, and case studies for electricity markets including decentralized P2P trading. Topics of interest for publication include, but are not limited to the following:

Solar power forecast methods and trading strategy;
Risk management techniques using financial instruments and/or weather derivatives;
P2P trading systems/networks and blockchain transactions;
Demand forecast and optimal consumption/power generation models;
Trading strategy of solar power output with storage/battery/EV systems;
Optimal network operation including renewable energy and power storage systems;

I am looking forward to your contributions.

Prof. Dr. Yuji Yamada
Guest Editor

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Keywords

Solar power/demand forecasting
Risk management techniques
P2P trading systems/networks
Decentralized electricity market
Weather derivatives and other derivative contracts

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Related Special Issue

Forecasting and Risk Management Techniques for Electricity Markets II in Energies (9 articles)

Published Papers (10 papers)

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Research

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24 pages, 6713 KiB

Open AccessArticle

Effectiveness and Feasibility of Market Makers for P2P Electricity Trading

by Shinji Kuno, Kenji Tanaka and Yuji Yamada

Energies 2022, 15(12), 4218; https://doi.org/10.3390/en15124218 - 8 Jun 2022

Cited by 9 | Viewed by 2892

Abstract

Motivated by the growing demand for distributed energy resources (DERs), peer-to-peer (P2P) electricity markets have been explored worldwide. However, such P2P markets must be balanced in much smaller regions with a lot fewer participants than centralized wholesale electricity markets; hence, the market has inherent problems of low liquidity and price instability. In this study, we propose applying a market maker system to the P2P electricity market and developing an efficient market strategy to increase liquidity and mitigate extreme price fluctuations. To this end, we construct an artificial market simulator for P2P electricity trading and design a market agent and general agents (photovoltaic (PV) generators, consumers, and prosumers) to perform power bidding and contract processing. Moreover, we introduce market-maker agents in this study who follow the regulations set by a market administrator and simultaneously place both sell and buy orders in the same market. We implement two types of bidding strategies for market makers and examine their effects on liquidity improvement and price stabilization as well as profitability, using solar PV generation and consumption data observed in a past demonstration project. It is confirmed that liquidity and price stability may be improved by introducing a market maker although there is a trade-off relationship between these effects and the market maker’s profitability. Full article

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

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15 pages, 1121 KiB

Open AccessArticle

Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis

by Faheem Jan, Ismail Shah and Sajid Ali

Energies 2022, 15(9), 3423; https://doi.org/10.3390/en15093423 - 7 May 2022

Cited by 56 | Viewed by 4926

Abstract

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order

P

is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models. Full article

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

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17 pages, 1720 KiB

Open AccessArticle

Bidding Agents for PV and Electric Vehicle-Owning Users in the Electricity P2P Trading Market

by Daishi Sagawa, Kenji Tanaka, Fumiaki Ishida, Hideya Saito, Naoya Takenaga, Seigo Nakamura, Nobuaki Aoki, Misuzu Nameki and Kosuke Saegusa

Energies 2021, 14(24), 8309; https://doi.org/10.3390/en14248309 - 9 Dec 2021

Cited by 6 | Viewed by 2681

Abstract

As the world strives to decarbonize, the effective use of renewable energy has become an important issue, and P2P power trading is expected to unlock the value of renewable energy and encourage its adoption by enabling power trading based on user needs and user assets. In this study, we constructed a bidding agent that optimizes bids based on electricity demand and generation forecasts, user preferences for renewable energy (renewable energy-oriented or economically oriented), and owned assets in a P2P electricity trading market, and automatically performs electricity trading. The agent algorithm was used to evaluate the differences in trading content between different asset holdings and preferences by performing power sharing in a real scale environment. The demonstration experiments show that: EV-owning and economy-oriented users can trade more favorably in the market with a lower average execution price than non-EV-owning users; forecasting enables economy-enhancing moves to store nighttime electricity in batteries in advance in anticipation of future power generation and market prices; EV-owning and renewable energy-oriented users can trade more favorably in the market with other users. EV-owning and renewable energy-oriented users can achieve higher RE ratios at a cost of about +1 yen/kWh compared to other users. By actually issuing charging and discharging commands to the EV and controlling the charging and discharging, the agent can control the actual use of electricity according to the user’s preferences. Full article

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

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12 pages, 4332 KiB

Open AccessArticle

Demonstration of Blockchain Based Peer to Peer Energy Trading System with Real-Life Used PHEV and HEMS Charge Control

by Yuki Matsuda, Yuto Yamazaki, Hiromu Oki, Yasuhiro Takeda, Daishi Sagawa and Kenji Tanaka

Energies 2021, 14(22), 7484; https://doi.org/10.3390/en14227484 - 9 Nov 2021

Cited by 14 | Viewed by 3518

Abstract

To further implement decentralized renewable energy resources, blockchain based peer-to-peer (P2P) energy trading is gaining attention and its architecture has been proposed with virtual demonstrations. In this paper, to further socially implement this concept, a blockchain based peer to peer energy trading system which could coordinate with energy control hardware was constructed, and a demonstration experiment was conducted. Previous work focused on virtually matching energy supply and demand via blockchain P2P energy markets, and our work pushes this forward by demonstrating the possibility of actual energy flow control. In this demonstration, Plug-in Hybrid Electrical Vehicles(PHEVs) and Home Energy Management Systems(HEMS) actually used in daily life were controlled in coordination with the blockchain system. In construction, the need of a multi-tagged continuous market was found and proposed. In the demonstration experiment, the proposed blockchain market and hardware control interface was proven capable of securing and stably transmitting energy within the P2P energy system. Also, by the implementation of multi-tagged energy markets, the number of transactions required to secure the required amount of electricity was reduced. Full article

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

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18 pages, 6542 KiB

Open AccessArticle

Feasibility Conditions for Demonstrative Peer-to-Peer Energy Market

by Reo Kontani, Kenji Tanaka and Yuji Yamada

Energies 2021, 14(21), 7418; https://doi.org/10.3390/en14217418 - 8 Nov 2021

Cited by 6 | Viewed by 2992

Abstract

Distributed energy resources (DERs) play an indispensable role in mitigating global warming. The DERs require flexibility owing to the uncertainty of their power output when connected to the power grid. Recently, blockchain technology has actualized peer-to-peer (P2P) energy markets, promoting efficient and resilient flexibility in the power grid. This study aimed to extract insights about the contribution of the P2P energy markets to ensuring flexibility through analyzing transaction data. The data source was a demonstration project regarding the P2P energy markets conducted from 2019 to 2020 in Urawa-Misono District, Japan. The participants in the project were photovoltaic generators (PVGs), convenience stores (CSs), and residences equipped with battery storage as the only flexibility in the market. We quantitatively analyzed the prices and volumes ordered or transacted by each participant. The execution prices purchased by the residences were lower than those purchased by CSs; the differences between execution prices and order prices of the residences were narrower than those of PVGs and CSs; the lower state-of-charge (SoC) in the storage battery induced the higher purchasing prices. Thus, P2P energy markets, where holding flexibility resulted in the advantageous position, can promote installing flexibility through market mechanisms. Full article

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

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28 pages, 79848 KiB

Open AccessArticle

Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on Power Markets

by Yuji Yamada and Takuji Matsumoto

Energies 2021, 14(21), 7311; https://doi.org/10.3390/en14217311 - 4 Nov 2021

Cited by 6 | Viewed by 2555

Abstract

In a competitive electricity market, both electricity retailers and generators predict future prices and volumes and execute electricity delivery contracts through power exchange. In such circumstances, they may suffer from uncertainties caused by fluctuations in spot prices and future demand due to their high volatility. In this study, we develop a unified approach using derivatives and forwards on the spot electricity price and weather data to mitigate the cashflow fluctuation for power utilities. We aim to clarify the applicability of our proposed methods and provide a new and useful perspective on hedging schemes involving various electricity utilities, such as power retailers, solar photovoltaic (PV) generators, and thermal generators. Moreover, we analyze the risk of risk takers (such as the insurance companies in this study) in the derivatives market. In addition, we perform empirical simulations to measure out-of-sample hedging effects on their cashflow management using actual data in Japan. Full article

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

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14 pages, 2001 KiB

Open AccessArticle

Designing a User-Centric P2P Energy Trading Platform: A Case Study—Higashi-Fuji Demonstration

by Yasuhiro Takeda, Yoichi Nakai, Tadatoshi Senoo and Kenji Tanaka

Energies 2021, 14(21), 7289; https://doi.org/10.3390/en14217289 - 3 Nov 2021

Cited by 14 | Viewed by 3580

Abstract

Peer-to-peer (P2P) energy trading is gaining attention as a technology to effectively handle already existing distributed energy resources (DER). In order to manage a large number of DER, it is necessary to increase the number of P2P energy trading participants. For that, designing incentives for participants to engage in P2P energy trading is important. This paper describes a user-centric cooperative mechanism that enhances user participation in P2P energy trading. The key components of this incentive for participants to engage in P2P energy trading are described and evaluated in this study. The goal of the proposal is to make it possible to conduct economic transactions while reflecting the preferences of the traders in the ordering process, making it possible to conduct transactions with minimal effort. As a case study, the Higashi-Fuji demonstration experiment conducted in Japan verified the proposed mechanism. In this experiment, 19 households and 9 plugin hybrid vehicles (PHV) were evaluated. As a result, the study confirmed that prosumers were able to sell their surplus electricity, and consumers were able to preferentially purchase renewable energy when it was available. In addition, those trades were made economically. All trades were made automatically, and this efficiency allowed the users to continue using the P2P energy trading. Full article

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

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22 pages, 52539 KiB

Open AccessArticle

Comprehensive and Comparative Analysis of GAM-Based PV Power Forecasting Models Using Multidimensional Tensor Product Splines against Machine Learning Techniques

by Takuji Matsumoto and Yuji Yamada

Energies 2021, 14(21), 7146; https://doi.org/10.3390/en14217146 - 1 Nov 2021

Cited by 7 | Viewed by 2949

Abstract

In recent years, as photovoltaic (PV) power generation has rapidly increased on a global scale, there is a growing need for a highly accurate power generation forecasting model that is easy to implement for a wide range of electric utilities. Against this background, this study proposes a PV power forecasting model based on the generalized additive model (GAM) and compares its forecasting accuracy with four popular machine learning methods: k-nearest neighbor, artificial neural networks, support vector regression, and random forest. The empirical analysis provides an intuitive interpretation of the multidimensional smooth trends estimated by the GAM as tensor product splines and confirms the validity of the proposed modeling structure. The effectiveness of GAM is particularly evident in trend completion for missing data, where it is able to flexibly express the tangled trend structure inherent in time series data, and thus has an advantage not only in interpretability but also in improving forecast accuracy. Full article

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

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24 pages, 7559 KiB

Open AccessArticle

Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness

by Takuji Matsumoto and Yuji Yamada

Energies 2021, 14(11), 3351; https://doi.org/10.3390/en14113351 - 7 Jun 2021

Cited by 8 | Viewed by 7698

Abstract

Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis. Full article

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

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Review

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23 pages, 695 KiB

Open AccessReview

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

by Hakan Acaroğlu and Fausto Pedro García Márquez

Energies 2021, 14(22), 7473; https://doi.org/10.3390/en14227473 - 9 Nov 2021

Cited by 34 | Viewed by 6604

Abstract

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions. Full article

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

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