Electricity Trading Research Articles

Potential gains of long-distance trade in electricity

Electrification of all economic sectors and solar photovoltaics (PV) becoming the lowest-cost electricity generation technology in ever more regions give rise to new potential gains of trade. We develop a stylized analytical model to minimize unit energy cost in autarky, open it to different trade configurations, and evaluate it empirically. We identify large potential gains from interhemispheric and global electricity trade by combining complementary seasonal and diurnal cycles. The corresponding high willingness to pay for large-scale transmission suggests far-reaching political economy and regulatory implications.

A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

Analysis of generation investments under price controls in cross-border trade of electricity

Price controls are commonly employed in Latin America and other regions to prevent price increases in exporting regions during cross-border trade of electricity. This paper introduces a methodology to evaluate the economic impact of price controls on generation investments, dispatch decisions, and carbon emissions in the context of cross-border electricity trade. We propose a non-linear adjusting function to represent a price-control rule, which can be incorporated into equilibrium models for generation firms and price-sensitive consumers. By employing fixed-point iterations between the adjusting function and an optimization problem that determines equilibrium investments and dispatch decisions for generation firms in a competitive environment, we obtain an equilibrium solution. We apply the proposed methodology to analyze the current price controls governing cross-border electricity trade between Chile and Argentina, with a focus on the exporting country. Our findings reveal that existing price controls can impede incentives for renewable energy investments, particularly in wind and solar, while stimulating investments in fossil fuel-based generation capacity. Moreover, these controls lead to increased CO2 emissions compared to a scenario without price controls. Additionally, we observe that the inefficiency resulting from price controls is proportionate to the volume of exports relative to local demand and the price elasticity of demand in the importing region.

Research on the environment & economic indicators for electricity trading

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design

Dynamic tariffs play an important role in demand response, contributing to smoothing power consumption and reducing generation capacity requirement and carbon emission. However, in the existing works, tariffs are usually designed without comprehensive consideration, such as potential user responses to tariffs. Thus, assuming an electricity trading market contains a utility company and multiple residential users, a dynamic tariff design method is proposed in this paper, considering user responses to tariff changes. Leveraging the non-intrusive load monitoring technique, rated power and user preference features for each appliance are acquired by the utility company to quantify user comfort (discomfort) based on derived user appliance usage habits. Then, a bi-level Stackelberg game model is built on the supply side for designing optimal dynamic tariffs and imitating the influence of tariff changes on DR plans for users. The upper level represents the utility company, trying to maximize utility profit, social welfare and carbon emission reduction. While the lower level represents users, aiming to minimize electricity bills and user discomfort. By solving such an optimization problem with multiple objectives, a novel hybrid probabilistic multi-objective evolutionary algorithm balancing evolutionary efficiency and stability is applied where random forest is adopted to boost performance. The proposed model is benchmarked with two state-of-the-art pricing methods and validated on a publicly accessible REFIT dataset, where low-rate power measurements are collected from real houses in the UK. The experimental results show the proposed model generally outperforms benchmarks on dynamic tariff design in achieving peak-shaving and low carbon emission while preserving user satisfaction. Furthermore, a case study is implemented, which verifies the necessity of various objectives employed in the proposed method.

Multiple Virtual Power Plants Transaction Matching Strategy Based on Alliance Blockchain

Virtual power plants can aggregate distributed energy resources and interruptible loads in a region for coordinated regulation and unified transaction. However, with the diversification of competition in the electricity market, the distributed operation mechanism between multiple virtual power plants (multi-VPPs) has gradually become a research focus. Based on this, this paper proposes a new type of distributed transactions strategy between multi-VPPs, i.e., the transaction matching mechanism. A two-stage transaction model based on the transaction matching is constructed for multi-VPPs to participate in the day-ahead and intraday electricity trading markets. In the first stage, each VPP optimizes its own internal units’ output and external interaction power through a cooperative game; in the second stage, it is the transaction matching among multi-VPPs that can match the most suitable counterpart by flexible price setting to increase the benefits of all the VPPs. Considering the efficiency and security of blockchain technology, we choose to complete the transaction matching between multi-VPPs with the support of alliance blockchain technology to improve the speed of system solution.

Security requirement classification of electricity trading data based on hierarchical fuzzy Petri network

With the emergence of spot electricity trading, market-oriented trading has been intensively carried out, forming a multi-cycle trading system. In this process, a large amount of fine-grained electricity trading data circulates on the trading platform. Trading data is an important basis for decision-making in the electricity spot market, which directly affects the trading profits of market entities, proper disclosure of this information is very important for market enterprises. Information disclosure must ensure the validity and security. However, it is hard to judge the security demand for trading data, and there is no suitable evaluation system for determining the security requirement of data, which will limit the electricity market’s further development. In this paper, we first design an indicator system for security requirements classification, which manages data risks from three aspects: data classification, data risk, and entity demand. This system will guide us in determining data security requirements and further help identify differentiated data security protection schemes. Then, based on this system, we propose the classification method of data security requirements through a hierarchical fuzzy Petri net. The lower network realizes a reasonable assessment of data risk with reference to the index system, and the upper network finally determines the level of security requirements through the fuzzy rule base. Last, two types of data are selected to judge their security requirements. The results show that our method can provide a reference for privacy protection in electricity market data.

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

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.

Local electricity market pricing mechanisms’ impact on welfare distribution, privacy and transparency

Local energy communities and electricity markets have emerged as possibilities for interaction among prosumers. A substantial effort has been invested into creating efficient pricing mechanisms for various market arrangements, all of which take into consideration distinct characteristics of local electricity trading. However, since they are all evaluated in terms of various systems and market conditions, it is challenging to directly compare the mechanisms. In this research, three well-established pricing mechanisms from the literature are systematically compared and evaluated under identical settings on their influence on welfare distribution across various market participant groups, privacy protection, transparency and complexity level. According to the findings, the supply–demand ratio pricing system leads to the lowest costs for consumers and is also the most privacy compliant and transparent. Furthermore, prosumers obtain the highest cost-savings through the consensus alternating direction method of multipliers pricing mechanism, whereas the equilibrium pricing mechanism performs best regarding economic fairness. The aim of this article is to provide insight into the performance of different pricing mechanisms to energy regulators and local electricity market facilitators. The comparative analysis should aid in making informed decisions on the implementation of local electricity markets.

Adaptive robust scheduling of a hydro/photovoltaic/pumped-storage hybrid system in day-ahead electricity and hydrogen markets

A hybrid energy generation system (HEGS) exploits the synergies of various energy to improve the utilization rate of renewable energy while enhancing economic efficiency. In deregulated energy markets, this study proposes a scheduling model for HEGS trading in both day-ahead electricity and hydrogen markets to maximize total profits based on price arbitrage. This HEGS is based on a real case in Southwest China and comprises three hydro units, two photovoltaic (PV) plants a pumped-storage facility, and an electrolyzer. The uncertainty of available PV power generation is modelled using polyhedral uncertainty set and then the proposed model is cast as an adaptive robust optimization (ARO) model in which the conservativeness can be controlled by the PV uncertainty budget (ΓPV). Then the column-and-constraint generation algorithm is employed to solve this model. The numerical results reveal that the total profit of HEGS trading in two markets can be increased compared to the case of only trading in the electricity market. For instance, the increase in the total profit is 1.01 % (i.e. 5091 CNY) when ΓPV = 2. Besides, the total profit is considerably affected by ΓPV. As ΓPV increases from 2 to 8 (i.e. the robustness increases), the total profit decreases from 507,045.5 CNY to 503,036.1 CNY. Moreover, sensitivity analysis of the total profit related to the pumped-storage capacity and hydrogen price are also presented.

LightGBM-Integrated PV Power Prediction Based on Multi-Resolution Similarity

Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can improve the accuracy of PV power prediction, this study proposes an integrated PV power prediction method based on a multi-resolution similarity consideration that considers both trend similarity and detail similarity. Firstly, using irradiance as the similarity variable, similar-days were selected using grey correlation analysis to form a set of similar data to control the similarity, with the overall trend of the day to be predicted at a macro level. Using irradiance to calculate the similarity at each specific point in time via Euclidean distance, similar-times were identified to form another set of similar data to consider the degree of similarity in detail. The above approach enables the selection of similarity data for both resolutions. Then, a 1DCNN-LSTM prediction model that considers the feature correlation of different variables and the temporal dependence of a single variable was proposed. Three important features were selected by a random forest model as inputs to the prediction model, and two similar data training models with different resolutions were used to generate a photovoltaic power prediction model based on similar-days and similar-times. Ultimately, the learning of the two predictions integrated with LightGBM compensate for each other, generating highly accurate predictions that combine the advantages of multi-resolution similarity considerations. Actual operation data of a PV power station was used for verification. The simulation results show that the prediction effect of ensemble learning was better than that of the single 1DCNN-LSTM model. The proposed method was compared with other commonly used PV power prediction models. In the data case of this study, it was found that the proposed method reduced the prediction error rate by 1.48%, 11.4%, and 6.45%, compared to the LSTM, CNN, and BP, respectively. Experiments show that model prediction results considering the selection of similar data at multiple resolutions can provide more extensive information to an ensemble learner and reduce the deviation in model predictions. Therefore, the proposed method can provide a reference for PV integration into the grid and participation in market-based electricity trading, which is of great significance.

Subsidy removal, regional trade and CO2 mitigation in the electricity sector in the Middle East and North Africa region

This study analyzes the impacts on the power sector in the Middle East and North Africa region of three policies: removal of fuel subsidies, enhanced cross-border electricity trade, and a cap on carbon dioxide emissions. The analysis uses a power system planning model that minimizes the total electricity supply cost over 2018–2035 by satisfying specified technical, economic, environmental, and policy constraints. The study shows that the region would save between US$26.3 billion and US$27.5 billion, measured in 2018 prices, by removing subsidies to natural gas used for power generation. Cross-border electricity trade would save US$83.6 billion to US$90.9 billion. The two policies together would yield a reduction of 10 percent in cumulative power-sector CO2 emissions in the region, with a net cost savings of US$111 billion. If a carbon emission-constraining policy is considered to achieve the same level of emissions reduction, the power supply costs will increase by US$97 billion. The study also reveals that the removal of fossil fuel subsidies and expanded cross-border electricity trade significantly complement each other and also to achieve climate change mitigation targets in the MENA region.

Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system’s operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency.

Research on Data Acquisition Technology of Intelligent Laboratory Based on Retinex-Net Image Recognition

The reliability of electric power metering verification data is related to the accuracy and fairness of electric power trade settlement. Most of the existing verification equipment uses traditional pointer-type electrical verification instruments. The verification process requires the manual recording of data and the issuance of verification reports. There are problems such as low data collection efficiency, high error rate, and human tampering, which lead to great uncertainty in the detection results. This paper presents a data acquisition technology of pointer-type electrical verification instruments based on Retinex-Net image recognition for power metering verification data acquisition of intelligent laboratory, which realizes automatic data acquisition of power metering verification, ensures the accuracy and reliability of verification results, and effectively supports the digital transformation and development of power metering verification field.

In Situ Calibration of Transmission System Power Grid Energy Meters Using Energy Measurements and Kalman Filter

Correct energy measurement is vital for electric energy trading. There are often stringent legal demands on the revenue metering that require periodic in situ substation verification. Traditional energy metering testing methods are not possible in IEC 61850 digital stations using a process bus. In this article, we will show that it is possible to use data analysis to verify the accuracy of energy measurement systems in substations. Our algorithms can be used as a part of the legal verification and runs remotely on-line. As a consequence, some of the periodic accuracy verification including substation visits can be abandoned. The on-line algorithms makes it possible to decrease the errors in both revenue metering systems and in other energy measurements systems in the Swedish transmission system. Measurement errors are now detected and amended much faster which reduces incorrect billing. The estimated value of incorrect billing is decreased by more than a million EUR annually. Our algorithms also provide a solution to bridge the problem to verify measurements in future fully digital substations.

Blockchain Based Optimized Energy Trading for E-Mobility Using Quantum Reinforcement Learning

Using microgrids to charge Electric Vehicles (EVs) is a significant step toward achieving Electric mobility. The microgrids generate electricity for self-use and sell surplus energy locally in Peer-to-Peer (P2P) manner, where seller and buyer meet to trade electricity directly on agreed terms without any intermediary. The energy trading decision for the microgrid is a challenging issue due to uncertainty of renewable energy yield, the electric demands of EVs and without knowing the offers of other competitive microgrids together makes it hard to decide the selling price of energy per unit. Further, there is a need to audit and verify the energy trading and store energy transactions securely in distributed manner to avoid collapse of the system in case of single point of failure. In this context, this paper presents a Blockchain and Quantum Reinforcement Learning based optimized Energy Trading (BQL-ET) model for E-mobility. Firstly, a double-auction mechanism is proposed to set optimal market-trading price by observing the selling price of each microgrid and the demand of EV's. Secondly, using smart contracts, consortium blockchain is deployed for the evaluation of overall utility, which includes energy supply, demand, and cost for both microgrids and EVs. Finally, Utility maximization problem is transformed into a Markov Decision Process (MDP), and in order to develop the learning policy and maximize overall utility, a QRL optimization for solving the MDP problem is proposed. Convergence analysis and performance results attest that BQL-ET convergences faster, maximizes the utility of both microgrids an EVs with lower transaction confirmation time and setting of the optimal market-trading price compared to state-of-the art models.

Tantalum trade structural dependencies are what we need: A perspective on the industrial chain

As an important rare metal, the demand of tantalum has been increasing in international trade. Understanding the evolution and characteristics of trade in the tantalum industry chain can help countries optimize their industrial structure and distribution. In this work, we applied complex networks at the global and local levels to analyze how tantalum industrial trade evolved from 2002 to 2020. Next, we used exponential random graph model (ERGM) to identify the factors of structural dependencies in tantalum trade networks. The results show that the country status in the trade network is constantly changing; however, China, the United States, and Germany remain at the center of the industrial chain. In addition, the transitivity of the network structure plays a fundamental role in the trade networks of the tantalum industry chain, which is its homogeneity. Meanwhile, the structural dependencies of the network of different products in the industrial chain are also heterogeneous. The tantalum ore and concentrates trade network (OCTN) is affected mainly by convergence and divergence, the transitivity of the network has the greatest impact on the unwrought tantalum trade network (UTTN), the tantalum electrical capacitors trade network (ECTN) is affected most by divergence, and the scrap trade network (WCTN) relies on more structural variables.

Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government’s incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption.

Modeling the transition to a zero emission energy system: A cross-sectoral review of building, transportation, and electricity system models in Canada

Models have long been effective tools in the planning and policy making of energy systems, but low-carbon electrification—decarbonizing generation supply while expanding electrical demand—poses new challenges for the modeling community. At its core, electrification relies on integrating insights that span the supply and demand side of power systems, resolving operational characteristics and long-term climate change, and the integration of previously independent engineered systems. However, our modeling landscape consists of a suite of models that focus on distinct sectors (power, buildings, transport) and spatial–temporal scales (municipal, provincial, federal). This paper probes whether the existing suite of energy system models, which span sectors, disciplines, and jurisdictions, is up to the task of charting net-zero pathways, specifically in the Canadian context. To do so, we analyze an inventory of energy system models that are being used in practice using a recently assembled model database. Next, we supplement our analysis with a web-based search and literature review. For each model category, we describe the key modeling approaches, strengths and weaknesses, and typical ways and areas in which these models are applied. We find that by focusing on a specific scale and sector, these models by their very definition, omit out-of-scope interactions leaving critical information gaps. Many of the most imperative areas for future research straddle multiple sectors or multiple scales—electric vehicle charging, carbon policy coordination, regional electricity trading, to name a few. Future research should focus on identifying ways in which different models could be used together to produce policy-related conclusions that are as detailed but more holistic than conclusions that can be gleaned from an individual model.

Modelling natural gas, renewables-sourced electricity, and ICT trade on economic growth and environment: evidence from top natural gas producers in Africa.

Addressing extensive global goals including growing energy-sourced electricity and advancing sustainable development plans strongly depends on natural gas as a transition fuel to renewable forms of energy. Therefore, by using pooled, random, and fixed-effects models, the current study investigates the effects of electricity sourced from natural gas (ENG), renewable energy (RE), and trade in information and communication technologies (ICTs) on economic growth and carbon dioxide (CO2) emissions in Africa's top three natural gas producers, Algeria, Egypt, and Nigeria, from 1990 to 2020. The findings indicate that CO2, ENG, ICT trade, and urbanization (UP) are all strongly and positively correlated to economic progress, with the exception of RE, which has an insignificant influence. For the environment, data indicate that RE and GDP degrade the environment while ENG and ICT trade boost it. The causality results that ENG and RE cause both economic growth and CO2 emissions. Based on these empirical results, it is recommended that policymakers should step up their efforts to usage natural gas as a transition fuel to renewable energy sources and acknowledge the advantages of the significant contribution that green ICT trade can make to economic advancement and a clean environment.