Electricity Trading Research Articles

A Coordinated Multi-Energy Trading Framework for Strategic Hydrogen Provider in Electricity and Hydrogen Markets

This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.

A Decentralized Market Model for a Microgrid With Carbon Emission Rights

Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The proposed trading model not only satisfies the demand for transactions but also ensures the constraint of total carbon emissions for the microgrid. Energy storage (ES) is introduced to balance loads more economically, and the constraint specifying that the ES cannot be simultaneously charged and discharged is proven to be satisfied automatically, ensuring the convexity of the model. Furthermore, by inserting local trackers to global constraints for each node, a scalable fully distributed algorithm is designed to solve the model locally for both global equality and inequality constraints. The proposed algorithm decomposes the arithmetic demand to each user without intermediate agents, which can effectively reduce the cost and ensure the transparency of trading. The algorithm is also proven to be convergent. Numerical results verify the effectiveness of the proposed market model.

Sustainability for all? The challenges of predicting and managing the potential risks of end-of-life electric vehicles and their batteries in the Global South

The transition from fossil-fuel-based internal combustion vehicles to electric vehicles plays a key role to decarbonize road transport and mitigate climate change. Even though this transition is still in its infancy, it is important to consider not only its environmental benefits but also its potential side effects. Recent projections estimate that the current electric vehicle fleet is expected to increase from 2.4 million in 2020 to over 81 million in 2050, when more than half of all new cars sold are predicted to be battery-electric vehicles (BEVs). End-of-life (EOL) BEVs and their components (particularly the batteries) are far more challenging to manage than their fossil-fueled predecessors as they contain large amounts of chemical substances that constitute potential hazards to the environment and human health and safety. The paper discusses relevant topics for understanding future risks of transition to electric mobility in the Global South countries, which include the internationally used vehicle fluxes; waste management challenges for EoL BEV and its lithium-ion batteries (LIB); environmental and human health impacts of EoL LIBs disposal and policies and regulations for the e-vehicle life cycle. Recommendations to support the development of science-based policies to close regulation gaps of the used electric vehicle international trade flow, avoid pollution-shifting and guarantee a sustainable transition to e-mobility in the Global South countries are given. As a conclusion, an integrated approach from international and national stakeholders is fundamental to guarantee strong policies and regulations as well as to support the development of a sound management of EoL EV and LIBs in the Global South countries and help pave the way to a global circular economy.

Ways of improving the efficiency of risk management in the context of integrated risk assessment of electricity industry enterprises

Methods. The theoretical and methodological basis of the research is the work of domestic and foreign scientists. The results were obtained due to the application of a system of methods: the method of logical analysis made it possible to develop the logic of the research; deductions – to identify individual factors affecting the risk level of the enterprise; generalization and synthesis – for the formation of a scientific approach to the integral risk assessment of electric power enterprises. The graphic method made it possible to visualize the obtained research results. Results. Domestic enterprises need the formation of approaches to risk management, which will allow them to ensure proper development and competitiveness. The article substantiates various approaches to risk grouping for electric power enterprises. The most important risks, according to the representatives of the electric power sector, are defined, among which the main ones are regulatory, operational and market risks. The authors prove that market risks in the electric power industry have a specific nature and are inherent only to national markets where there are competitive electricity trades. An approach to the integrated risk assessment of an electric power company has been developed, which is based on: calculation of financial and economic indicators of the company's activity; comparing them with normative values, assigning point values to the calculated coefficients; calculation of intra-group point values of coefficients of the financial and economic condition and calculation of the integral risk indicator of the enterprise's activity. Approbation of the scientific and methodical approach was carried out for the Khmelnytskyi electric power company and it was identified by the level of risk. Novelty. There is substantiated the scientific and methodological approach to the integrated risk assessment of enterprises as a tool for increasing the efficiency of risk management of enterprises in the electric power industry. Practical value consists in the possibility of applying the developed scientific and methodical approach to the integrated assessment of risk by enterprises of various industries. The obtained results allow domestic enterprises to participate in risk management processes and at the same time ensure competitive advantages in the relevant markets.

Peer-to-Peer Energy Trading Under Network Constraints Based on Generalized Fast Dual Ascent

The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.

Statistical arbitrage trading across electricity markets using advantage actor–critic methods

In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies.

Carbon Reduction Countermeasure from a System Perspective for the Electricity Sector of Yangtze River Delta (China) by an Extended Logarithmic Mean Divisia Index (LMDI)

The electricity sector is a complex system, especially in the Yangtze River Delta (YRD) of China. Thus, the carbon dioxide (CO2) emission of YRD’s electricity sector during 2000–2020 was first calculated and then evaluated from two systematical dimensions of cross-region and the whole process (production, trade, transmission, and consumption) by an extended logarithmic mean Divisia index (LMDI). (1) During 2000–2020, the CO2 emission of YRD’s electricity sector increased from 228.12 Mt to 807.55 Mt, with an average annual growth rate of 6.52%. Compared to other regions, the YRD’s electricity mix effect had the strongest mitigation impact on CO2 growth. Therefore, it is important for YRD to build a low-carbon electricity system itself, including the de-carbonization of electricity production and the carbon reduction of the electricity-use process. (2) Nationally, electricity trade had an overall mitigating impact on emission growth during 2000–2020. This result means that cross-regional cooperation or trade in the electricity sector is beneficial to emission reduction. So, it is important to improve the national power grids to promote trade. (3) Jiangsu had the largest CO2 emissions, while Anhui had the fastest average annual growth rate (9.71%). Moreover, the economic activity effect was the most significant driver in all provinces, especially in Jiangsu and Anhui. Thus, Jiangsu and Anhui should strive to improve the quality of economic growth while vigorously cutting carbon emissions. (4) Electricity transmission loss had an overall driving impact on emission growth in each YRD province, especially in Zhejiang and Anhui. Meanwhile, electricity structure, electricity trade, and electricity intensity were the inhibiting factors. Particularly, the inhibiting effect of Shanghai’s electricity structure was notably weak (−2.17 Mt). So, Shanghai should try hard to increase the proportion of renewable energy, while Zhejiang and Anhui should upgrade their electricity transmission equipment.

Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review

Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.

Microgrid trading mechanism enhancement for smart contract considering reputation values

ABSTRACT In order to reduce the system instability caused by credit risk in microgrid transactions in the blockchain, we propose a smart contract microgrid transaction model considering reputation value. Considering the instability caused by credit risk, the reputation factor is introduced to ensure the secure and stable operation of the microgrid energy trading system. The effectiveness of the scheme is verified by comparing traditional electricity trading and the trading with the introduced credit value scheme through simulation experiments.

Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Cloud energy storage (CES) receives increasing attention as an efficient and viable paradigm for the provision of distributed energy storage services. This paper exploits CES’s service modes to both energy storage and electricity trading for its users, e.g., microgrid (MG). The optimal day-ahead bidding strategy is investigated for CES as an independent entity in the electricity market. Besides, two energy service modes are introduced considering MG's requirements and preferences. Each mode consists of a set of schemes for energy storage system (ESS) rent (power-based and capacity-based) and electricity trading (internal price with an improved pricing method and market clearing price). Finally, a stochastic programming (SP)-based optimization model is formulated to maximize the CES’s expected profits fully considering the electricity market’s settlement mechanism and uncertainty as well as the participation interest of MG. The proposed solution is extensively assessed through a case study of CES’s energy services to five heterogeneous MGs with distinct electricity generation and consumption characteristics and two energy service modes are assessed through comparative experiments. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.

Energy Trading Strategy of Distributed Energy Resources Aggregator in Day-Ahead Market Considering Risk Preference Behaviors

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China’s power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China’s power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs’ perception of “gain” and “loss” with market risks in China’s two-tier market environment, the prospect theory and the marine predator’s algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators’ participation in electricity trading.

Multi-time scale stochastic production simulation under VHVDC long-term contract trading electricity quantity constraint

A two-layer multi-time scale stochastic production simulation framework is constructed to account for the long-term contract electricity quantity of ultra-high voltage direct current (UHVDC) transmission. On the upper layer, based on the characteristics of load demand and renewable energy output extracted from the historical operating data, monthly and daily production simulation models are carried out considering the seasonal characteristics of hydropower during a high-water period and low-water period to optimize the distribution of contract electric quantity sending through UHVDC transmission in the target year or month. According to the DC transmission electric quantity optimized by the daily production simulation in the upper layer, together with the forecast scenario, the lower layer of the framework provides the optimization of day-ahead scheduling and intra-day rolling dispatch in the implementation process. The day-ahead dispatch optimization makes full use of the adjustment capability of transmission and optimizes the DC transmission electric quantity correction. Its compensation is based on the result of the daily production simulation, then the correction will be returned to the upper layer to restart the optimization of the remaining UHVDC contract electric quantity of the subsequent period and its distribution plan. Combined with the day-ahead DC transmission plan, the intra-day rolling optimization is carried out to adjust the output of the unit using more accurate forecasting scenarios. The distributionally robust optimization model is used in the lower layer to convert an uncertain problem into a deterministic quadratically constrained quadratic programming (QCQP) problem according to the form of an uncertain distribution set. Then the QCQP problem is further converted into a linear programming (LP) problem by using the reformulation linearization technique (RLT). A test system with the energy composition and distribution referring to a real provincial power grid in northwest China is established for verification. The results show that the proposed method can effectively improve the economics of system operation and the accommodation of renewable energy based on ensuring security.

AROA: Adam Remora Optimization Algorithm and Deep Q network for energy harvesting in Fog-IoV network

Electric Vehicles (EV) has gained immense popularity due to the increasing awareness amongst people regarding low carbon emission. Smart vehicles have become a central part of the Internet of vehicles (IoV) infrastructure. Whenever several vehicles distribute its tasks, then the classical centralized model meet various issues, such as security and delay in communication. This paper devises a technique for energy harvesting in the Fog-IoV network. The Fog-IoV network simulation is done for enhanced processing. The three layers, such as fog layer, cloud layer, and IoV layer are adapted for electricity trading. The power prediction is performed with a deep reinforcement learning technique, namely Deep Q network (DQN). The optimal electricity trading is done with the proposed Adam Remora Optimization Algorithm (AROA). The AROA is obtained by the amalgamation of Remora Optimization Algorithm (ROA) and Adam optimization algorithm. The EVs represents buyer of electricity that demands electricity in such a way that Road side unit (RSU) perform bidding. The fitness function is newly modelled using predicted power, price, and distance. The experimentation of the technique is done in terms of fitness, power, and pricing. The proposed AROA-based DQN offered enhanced performance with the highest power of 11.920 and the smallest pricing of 16.949%.

Evaluating Peer-to-Peer Electricity Markets across the U.S. Using an Agent-Based Modeling Approach

The diffusion of distributed energy resources can overcome some challenges associated with the historical centralized model of electric power distribution. Decentralized generation by residential solar photovoltaic cells creates the potential for peer-to-peer (P2P) electricity trading, where households can act as consumers and prosumers to buy and sell renewable electricity. P2P energy markets are emerging at locations across the globe, and market performance is affected by various social, economic, and environmental factors. This research applies an agent-based modeling (ABM) framework to simulate electricity trades between heterogeneous households in a decentralized market. The P2P system is tested for 15 locations in the United States that vary in climate parameters and local economic factors. The results from these simulations are compared to assess how differences in climate, demand pattern, retail rate, and irradiance affect market performance. Simulations demonstrate that market outcomes rely on the ratio of prosumers to consumers, environmental factors, and geographic conditions. Battery energy storage overcomes limitations associated with faulty forecasting and improves the flexibility of household-generated solar resources to increase the proportion of production that is sold in the P2P market. The application of the agent-based modeling framework demonstrates how P2P markets can be expected to perform for various locations and can be applied to assess alternative locations for market performance.

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Assessing the Co-movements Between Electricity Use and Carbon Emissions in the GCC Area: Evidence from a Wavelet Coherence Method

This paper investigates the association between CO2 emissions and a range of factors, including electricity consumption, economic growth, urbanization, and trade openness for six Gulf Cooperation Council (GCC) countries using data covering the 1965–2019 period. Namely, Oman, Saudi Arabia, the UAE, Kuwait, Bahrain, and Qatar. Contrasting with the standard literature, our empirical strategy uses the wavelet coherence approach on the frequency domain, thought to complement the time series econometric procedures reported earlier on this topic. Supplied at the country level, associated evidence presents far-reaching policy recommendations whose applications may directly benefit environmental planning and bring high information value for the sake of sustainable energies in the Gulf region.

Financial and Economic Stability of Energy Sector Enterprises as a Condition for Poland’s Energy Security—Legal and Economic Aspects

The energy security of each country is one of the main factors of its proper functioning. Currently, in the era of problems related to energy security resulting from, among other things, the war in Ukraine, this topic is particularly important. This article presents issues related to Poland’s energy security, understood as the financial and economic stability of enterprises operating in the energy industry. This stability is considered in two aspects: macroeconomic, where the focus is mainly on the aspect of state intervention in market processes; and microeconomic, where factors determining the financial security of energy enterprises were identified, including internal and external factors affecting the functioning of these entities. In order to achieve the assumed research goals, the analysis of the indicated problems was based on non-reactive research, consisting in the assessment of the available information. It included studies of normative acts, official statistical data, industry reports and analyses, as well as data obtained in the form of a public information request. Two basic research methods were used in the work—dogmatic–legal and comparative analyses. The identification of factors affecting the security of companies in the sector was carried out on the basis of data on the entire energy sector in Poland for the years 2015–2021 on a semi-annual basis. Vector-autoregressive models were used for the analysis. As a result of the analyses, it was established that market failures and public safety are the premises justifying the public financing of enterprises in the electricity generation, transmission, distribution and trade sectors. At the same time, the conducted research showed that the level of financial security of energy enterprises in Poland was affected by the ratio of the value of goods and materials sold to net sales revenue, as well as the level of EBIT (earnings before deducting interest and taxes) margin, and among external factors, the level of GDP (gross domestic product), CPI (consumer price index) and Crude Oil were important.

Water-saving co-benefits of CO2 reduction in China’s electricity sector

Electricity sector is the largest CO2 emitter and water user in China's industrial sectors. The low-carbon transition of China's electricity sector reduces its cooling water consumption. Here we firstly quantify CO2 emission and virtual water embodied in electricity trade with Quasi-Input-Output model. Then, we analyze the impacts of energy substitution, efficiency improvement, and electricity trade on water-saving co-benefits of CO2 reduction with the differences between the baseline scenario and counterfactual scenario. Results show that the low-carbon transition contributes to water-saving in China's electricity sector. Virtual water and embodied CO2 have relatively decoupled from electricity trade since 2012. Water-saving (+10.4% yr-1) outweighed CO2 reduction (+8.4% yr-1) through energy substitution and efficiency improvement in the 'new normal' stage. Our work emphasizes the need to integrate water-saving co-benefits of CO2 reduction into electricity system planning and highlights the challenges to facilitate coordinated development of the electricity-water nexus in China.

Exploring the participation willingness and potential carbon emission reduction of Chinese residential green electricity market

China officially established a green electricity trading market in 2021. This study investigated the intention of Chinese households to participate in the green electricity tariff and surveyed their acceptable maximum premium; afterwards, the corresponding potential carbon emission reduction is evaluated. The survey shows that 82% of Chinese households support the additional willingness-to-pay (WTP) for green electricity. The mean willingness-to-pay values without protest-zeros are evaluated as RMB14.6 with a gender differential that men prefer to pay more than women. Relative impact factors analysis shows that household' intention of using green-e significantly impacts their participation in extra WTP, but households' income does not show a significant effect. On the other hand, the model for extra expenditure shows the opposite effect of these two factors. Last, the study conducted a scenario analysis to evaluate the potential emission reduction and related welfare transformation efficiency. The scenario analysis reveals that households’ green electricity trading potentially contributes to 30%–54% of the carbon reduction and a reasonable trading mechanism could achieve emission reductions goal with less welfare loss. Finally, some policy implications are proposed, providing theoretical support for policy-makers.

CROATIA AND ELECTRICITY TRADING IN CENTRAL EUROPE

This article will discuss electricity trading in Central Europe, with particular emphasis on Croatia. Croatia is a terminal and also a transit hub: a unique combination on the Central European electricity market. It will be explained how cross-border capacity auctions influence prices on the Central European electricity market, followed by a discussion about the most important factors that are significant for Croatia’s electricity trading strategy. Finally, the article will describe the current HEP electricity tendering process and make some recommendations for possible improvements in the future.