Spot Market Research Articles

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

A Basic Study on an Electricity Market Model Integrating the Interaction between Spot and Reserve Market

A Basic Study on an Electricity Market Model Integrating the Interaction between Spot and Reserve Market

Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability.

Hybridization of Concentrated Solar Thermal, Geothermal, and Biomass: Case Study on Kizildere-2 Geothermal Power Plant

The usage of fossil fuel in the energy sector is the primary factor for global GHG emissions, so it is crucial to better utilize RE sources. One way to do that is to hybridize RE technologies to make up for their deficiencies while enabling a more synergistic power production. This study utilizes such an approach to hybridize the KZD-2 Geothermal Power Plant (GPP) with CST and biomass in the southwest region of Turkiye. The main motivation is to address the two main issues of GPPs—excess turbine capacities happening over the operating years and decreasing performance during hot summer months—while also increasing the flexibility of KZD-2. A topping cycle of CST–biomass is added utilizing a PTC field as the CST technology and olive residual biomass combustion as the biomass technology. The hybrid plant is simulated on TRNSYS, and the energetic data show that it is possible to generate more than 20 MWe of additional power during sunny and clear sky conditions while also increasing the Capacity Factor (CF) from 69% to 74–76%. Moreover, the financial results show that the resulting LCOAE is 81.19 USD/MWh, and the payback period is five or nine years for using the YEKDEM incentive or the spot market prices, respectively.

Optimization model for distributed energy trading based on a market value allocation mechanism in the electricity spot market

This study proposes a novel distributed energy trading market model with a value distribution mechanism to optimize the allocation and transactions of distributed energy resources (DERs). The framework incorporates a direct load management approach via an electricity aggregator agent, simplifying market processes and reducing transaction costs. A Nash bargaining model is employed to design a fair and efficient value distribution mechanism, promoting equitable benefit allocation among participants. The model integrates stochastic programming to account for uncertainties in real-time load and DER output, enhancing its robustness and applicability in real-world scenarios. The proposed mechanism quantifies each DER’s contribution using a market value contribution rate, serving as a foundation for the Nash bargaining model. This approach ensures individual rationality for both the aggregator and DERs while maximizing overall system benefits. Case studies validate the model’s effectiveness, demonstrating improvements in resource utilization and fair benefit allocation. This research contributes to the advancement of distributed energy markets, offering valuable insights for designing efficient and equitable market structures, ultimately promoting grid stability, renewable energy adoption, and the development of more sustainable and flexible energy systems.

Determinants of the Factors Affecting the Governance Structure Choice of the Pineapple Value Chain: A Case Study on Market-based Agroforestry in Bangladesh

This study examines the governance structures selected by pineapple farmers in the agroforestry system of Bangladesh and the determinants that impact their choices for these structures. To evaluate the factors influencing farmers' decisions to participate in spot markets, bilateral agreements, or association transactions, the study utilizes both quantitative and qualitative approaches, including surveys of 200 farmers, 15 focus group discussions (FGDs), and 2 key informant interviews (KIIs). In order to choose individual participants, the study employed a stratified random sampling technique. Utilising a multivariate probit (MVP) model, the study reveals crucial factors like age, education, farming experience, farm size, market certainty, weather influence, market connection, and pre-delivery conditions. Analysis indicates that spot market is the most favored governance structure (GS) among the three options in the agroforestry system, followed by bilateral transactions and association agreements. Evidence suggests that younger and less educated farmers tend to favor associations, whereas more experienced farmers tend to favor bilateral partnerships. Farms of bigger size typically refrain from joining groups, and unfavorable weather circumstances decrease the probability of engaging in spot markets. The study also demonstrates that robust market links exert an impact on the selection of governance frameworks. Considering the results, the study suggests enhancing training programs to facilitate farmers' understanding and involvement in associations, motivating farmer associations to appeal to larger farms and small farmers, and enhancing market information systems to establish robust market connections.

Optimal scheduling strategies for electrochemical energy storage power stations in the electricity spot market

IntroductionThis paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market.MethodsThe model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations.ResultsBy examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years.DiscussionThe study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy.

The Influence of Speculation in Food and Commodities and the Impact of Futures Prices on Spot Prices- Evidence from two Behavioral Experiments

This paper examines the impact of speculation on commodity and food futures on spot prices. Econometric studies contradict each other, coming not to a clear empirical outcome. Therefore, we take an alternative methodological approach with two behavioral experiments. The outcome shows, that tendencies in futures prices influence the price-setting behavior of market players in the spot market in the same direction. Since futures prices had no direct influence on profits and there were no other influences, futures prices must have influenced the expectations of market participants psychologically. Speculation with futures prices thus has an influence on spot prices via expectations. When speculation involves commodities and food in the future market serving crucial real economic functions it can lead to substantial misallocations and damages, why speculation should be regulated.

Optimal price-taker bidding strategy of distributed energy storage systems in the electricity spot market

Optimal price-taker bidding strategy of distributed energy storage systems in the electricity spot market

Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks

A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.

Optimizing Price Markup: The Impact of Power Purchase Agreements and Energy Production Uncertainty on the Economic Performance of Onshore and Offshore Wind Farms

Wind energy is rapidly expanding its capacity as part of the global energy transition. To ensure economic viability, wind energy projects increasingly rely on risk mitigation strategies. While Power Purchase Agreements (PPAs) manage spot market price variability, wind farms still face energy production uncertainty, directly impacting the quantity of energy generated. This study presents a comprehensive analysis of the price markup for onshore and offshore wind farms, considering energy production uncertainty under PPA and non-PPA scenarios. The study examines key metrics such as equilibrium prices, price markups, Internal Rate of Return (IRR), Net Present Value (NPV), and their correlation to energy production risk. Results demonstrate that PPAs significantly alter the relationship between price markup and variability. Offshore wind farms can potentially benefit more from PPAs compared to onshore, especially at lower levels of wind energy variability. However, as variability increases, the risk mitigation provided by PPAs diminishes, and both onshore and offshore wind farms may require higher price markups for financial viability. These findings highlight the necessity of carefully designed PPA structures and pricing strategies to ensure long-term competitiveness and sustainability of wind energy projects.

Glimpse on Spot and Derivative Market of Electricity and Nepal’s Hydropower

Energy plays a vital role in a country’s economic development. Nepal abundantly blessed with various renewable resources like hydropower, solar energy, biogas, and biomass can achieve prosperity both economically and socially. Further, with a significant opportunities for the regional electricity market as the country has been surrounded by big powerhouses like China and India. It has huge possibilities for regional trading of renewable energy. The present total Installed capacity of power is 3157 MW in Nepal. With 95% hydropower generation, the energy mix in the Integrated Nepal Power System makes the country a surplus of electricity during the wet season when the water in the river is very high and a shortage during the dry season when the discharge in the river is minimal. Hence, to address the surplus generation during the wet season and shortage of power during the dry season, trading in the region plays a very vital role in balancing the demand and supply of the country. Nepal has been importing power during the dry season power deficit in the market during the dry season when the demand is high leaving NEA at risk of interrupting the supply to its customers. Likewise, uncertainty about clearing a bid during the wet season when the power production is high and Nepal is in the position to export the power in the regional market, as the Hydropower plant has to compete with the cheap coal power plant resulting in financial loss and uncertainty of return on investment. Hence, to tackle the mentioned financial risks, the Electricity Derivative market can be one of the best options as derivatives markets can be used to hedge against price fluctuations in electricity spot markets. Similarly, the uncertainty of power sale can be avoided by selling or buying the electricity through a derivative market for the future time period that secures the price and the amount that can be sold in the market in the future. In a nutshell, the Electricity Derivative market can be a useful mechanism for the development of hydropower in Nepal in the future.

The Impact of Participation Ratio and Bidding Strategies on New Energy’s Involvement in Electricity Spot Market Trading under Marketization Trends—An Empirical Analysis Based on Henan Province, China

As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China’s electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important.

Volatility Spillover Effect of NCDEX Spot and Futures Prices in Spices

This research is an attempt to assess the volatility in spot and futures prices in spices on NCDEX. Spot price and futures price Volatility is prominent indicators of the commodity futures market to protect the interest of beneficiaries and to hedge sharp price fluctuations in commodity markets. The study tested price patterns using data with 1777 observations for FY2015 to FY2022 daily spot and futures price series of near month contracts. VECM test and EGARCH were computed to examine spot and futures prices for three selected spices, namely jeera (cumin), turmeric and coriander. The results revealed that Turmeric and Coriander had significant bidirectional relationship and Jeera had significant unidirectional relationship between futures and spot market prices. Higher half-life (days) values of futures prices indicates more stability than spot prices of selected spices. Higher asymmetric effect value of spot market indicates higher volatility impacts because of noisy shocks than futures market.

Two-stage coordinated scheduling of hydrogen-integrated multi-energy virtual power plant in joint capacity, energy, and ancillary service markets

With the emergence of distributed resources (DRs), developing an effective, profitable, and green management method is of great importance. Virtual power plant (VPP) can aggregate various DRs, optimize their schedules, and participate in electricity markets uniformly to earn profit. The optimal scheduling problem in spot market has been widely studied, however, few studies have extended the services of VPP to the long-term scale. This paper proposes a coordinated scheduling method for a multi-energy virtual power plant (MEVPP), considering the integration of hydrogen facilities. A holistic market framework, including capacity market, energy market (EM), and ancillary service market (ASM) is developed jointly for the coordination among the annual capacity schedule, daily energy schedule, and daily reserve schedule of MEVPP. A hybrid hydrogen storage system including daily hydrogen storage and seasonal hydrogen storage is modeled to achieve both intraday and cross-seasonal peak shaving. Stochastic programming is used to tackle the uncertainties and risk management is considered by conditional value at risk. With the help of numerical analysis in case studies, it can be concluded that the proposed coordinated scheduling method can improve the economy of MEVPP. The profit from joint market decreases the total cost by about 52 %. The capacity adequacy and flexibility of MEVPP can be improved by the coordination of capacity and reserve services. The hybrid hydrogen storage system can realize the efficient integration of hydrogen and increase hydrogen production and consumption by about 9 %.

Autopsy of a futures market failure: Japan’s Dojima rice futures in the early 21st century

Futures markets promise improved price transparency and risk mitigation for staple commodities important to many developing countries, but they can fail even in developed economies with modern financial institutions. We investigate rice futures trading in Japan at the Osaka Dojima Exchange to identify the reasons underlying its failure in 2021. Our analysis reviews the multiple necessary conditions for a successful commodity futures market. We find the Japanese rice market did not meet many of these conditions. The spot market was fragmented with heterogeneous goods and a single dominant firm. Futures trading was unable to facilitate price discovery or effective hedging activity. To the extent it was traded, the Dojima rice futures contract was instead used as a forward contract for making and taking physical delivery. In addition, there was political opposition and regulatory uncertainty around futures trading. Using detailed data on trader positions in the futures market, we show how these factors contributed to diminished interest in futures trading from firms who handle physical commodities. The lessons from the Japanese case are useful for traders, exchanges, and policy makers, especially those in countries where futures markets may face similarly unfavorable economic and political conditions.

Problems and Countermeasures of Apple Agricultural Products Futures Market in China

In recent years, the improvement of quality and efficiency of national economy and agricultural modernization have directly promoted the innovative development of apple industry in China. At the end of 2017, the first Apple futures in China was born, and then the scale of Apple trading market continued to expand, and the standardization of Apple futures trading market increased year by year, which directly promoted the adjustment and upgrading of Apple industry. However, the development of Apple futures market lags behind the current situation of spot market development, and the imperfect risk management system, low internationalization level of Apple futures, imperfect laws and regulations of Apple futures market and chaos of market speculators still need to be solved urgently. In order to promote the rapid development of China's apple futures market, it is important to improve the trading system of bulk agricultural products; Strengthen the risk management mechanism; Strengthen international market cooperation and exchanges; Revise and improve the relevant laws and regulations of the Apple Futures Market and improve the level of market standardization; Take measures to limit speculation.

Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector

Promoting the widespread adoption of multi-complementary low carbon power generation technologies is a fundamental strategy for attaining carbon neutrality within the electricity sector. In the context of electricity market reform, this study develops an agent-based modeling framework integrated simulation with optimization. The model uses agent-based simulation to analyze annual market dynamics and low-carbon technology diffusion, with a two-stage optimization for energy storage and spot market simulation. The research findings indicate: (1) For different power generation technologies, the spot market can establish differentiated average prices, making the market price of carbon-captured coal power higher than that of conventional coal power, which are advantageous in reducing the subsidies required to promote the diffusion of carbon capture technology. (2) Expanding the spot market scale and implementing differentiated capacity compensation mechanisms both promote carbon capture technology diffusion. Nevertheless, with the marginal clearing mechanism, thermal power holds a average price advantage than renewable energy. Failing to control the growth of thermal power capacity will result in increased carbon emissions. (3) After 2030, energy storage's role in balancing supply and demand grows. Storage capacity should align with renewable energy scale and the regional characteristics of wind and solar resources to prevent overbuilding and stranded assets.

Volatility Spillover of Crude Palm Oil (CPO) Commodity between Spot and Futures Market in Indonesia

The development of trade globalization has led to the expansion of market interactions between countries, specifically for Crude Palm Oil (CPO) commodities. This study aims to determine the volatility of CPO prices between the Medan spot market and the futures market consisting of the Jakarta futures market, Malaysia futures market, and the Rotterdam forward market. The daily data on CPO prices in each market from January 2016 to December 2020. The data analysis method uses the GARCH model (1.1). The results showed a volatility transmission from the entire futures market to the Medan spot market. Thus, the CPO commodity futures market influences price volatility and the price formation process in the Medan spot market. Therefore, CPO commodity market players must be alert to the information occurring in the futures market to determine investment strategies.

Study On Operation Strategy Of CSP Peak-Regulating Power Station Based On Spot Trading Market In Gansu Province

On March 3, 2022, the National Development and Reform Commission of China and the National Energy Administration of China jointly published the Notice on Accelerating the Construction of the Electricity Spot Market. The notice indicated that the government would support the uninterrupted operation of the power spot pilot project with conditions and form a long-term and stable electricity spot market as soon as possible. These steps will help rapidly improve China's electricity spot market construction. Due to its large-scale energy storage capacity, the Concentrated Solar Power (CSP) station can be used as a peak-regulating power station to provide grid-friendly long-term peak regulating capacity and moment of inertia for the power system. When CSP stations participate in electricity spot market trading, they can optimize their operation strategy to generate more electricity during periods with higher electricity prices, thereby obtaining more profits. In this paper, a 100MW CSP Peak-Regulating Station with an 80.0hm2 reflecting area of Heliostats Field (HF) is designed in Gansu, China. In the Peak-Regulating operation strategy, the most suitable TES Capacity for this project is determined to be 15h. We take this CSP station as the research objective to optimize the operation strategy of the power station. The research results indicate that optimizing the operation strategy will increase the power station's annual electricity sales revenue by about 19.97% before the strategy was optimized when the power station participated in the real-time trading of the Gansu electricity spot market in 2022.