Electricity Trading Research Articles

Energy trade tempers Nile water conflict

The demand for energy, water and food in Africa continues to increase, resulting in growing pressure on contentious multisector resource systems like the River Nile. The ongoing dispute over Nile resources could become a zero-sum game if addressed from a water-centric viewpoint. Understanding how energy system management impacts water infrastructure introduces new opportunities to solve water conflicts. Although benefit-sharing of water resources in the Nile Basin has been promoted to counteract water volume disputes, it has not yielded actionable solutions to the toughest negotiations over the past two decades. Here we develop a detailed and integrated energy–river basin system simulator of 13 East African countries, including the Nile Basin, and show how new electricity trade agreements between Ethiopia, Sudan and Egypt could help resolve the ongoing water dispute over the Grand Ethiopian Renaissance Dam. The results show that increasing energy trade can reduce Egyptian water deficits, reduce regional greenhouse gas emissions, increase hydropower generation in all three countries, reduce energy curtailment in Sudan and increase Ethiopia’s financial returns from electricity. This study underscores how spatial quantification of river–energy system interdependencies can help decision-makers find actionable multisector benefit-sharing solutions.

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

Establishing a hierarchical local market structure using multi-cut Benders decomposition

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales.

Infrastructure adequacy for electricity trading in East Africa

A well-designed and managed electricity supply infrastructure system is essential for integrated energy market. This paper tracks progress on infrastructure for electricity trading in East Africa integrated electricity market. Using data on electricity infrastructure targets in Master Plan 2013-2023 and actual infrastructure delivered by 2022, we conducted earned value analysis (EVA) to establish whether the completed generation and transmission infrastructure can adequately facilitate electricity trading across EAC countries. Findings show that by 2022 the region had realized 54% of the 12,567MW planned generation capacity and 211% of transmission network targets. Investment inflows for infrastructure have been faster than anticipated with actual variance of 325%. This triggered 47% earned value in surplus load worth US$357million of trade, despite actual electricity trading not happening at the same pace. We construed some merit-order conditions for iterative planning to synchronize generation infrastructure with transmission infrastructure for trade efficiency.

Electricity grids and geopolitics: A game-theoretic analysis of the synchronization of the Baltic States’ electricity networks with Continental Europe

Can supply of electricity be used as an energy weapon? This question rises in importance as more countries trade electricity across borders to smooth out grid stability issues and improve cost-efficient dispatching across large geographic areas. In the context of the Baltics’ disentanglement from the BRELL (Belarus, Russia, Estonia, Latvia, and Lithuania) electricity grid and synchronization with Europe, we develop a game-theoretic model and examine the strategies of the involved parties in light of the potential for electricity trade to be used as a geopolitical weapon. We conceptualize the process of the synchronization project as a sequential-move game between three actors (Russia, the Baltic states, and the EU-U.S.). Our findings suggest that, in large part due to reputational concerns, Russia is unlikely to cooperate in the synchronization process. Instead, our model predicts that Russia will not wait for the Baltic states to complete their synchronization with the European grid before disconnecting them from the BRELL system. Direct Russian cyberattacks against Baltic grids are not implemented and the Baltics do not have to make concessions, provided that Western allies signal a strong likelihood of deterrent retaliation. We offer policy recommendations for Europe, Russia and the Baltic states.

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

AbstractThe increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low‐carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer‐to‐peer (P2P) electricity and carbon trading in multi‐energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value‐at‐risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision‐making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

Frequency regulation with storage: On losses and profits

Low-carbon societies will need to store vast amounts of electricity to balance intermittent generation from wind and solar energy, for example, through frequency regulation. Here, we derive an analytical solution to the decision-making problem of storage operators who sell frequency regulation power to grid operators and trade electricity on day-ahead markets. Mathematically, we treat future frequency deviation trajectories as functional uncertainties in a receding horizon robust optimization problem. We constrain the expected terminal state-of-charge to be equal to some target to allow storage operators to make good decisions not only for the present but also the future. Thanks to this constraint, the amount of electricity traded on day-ahead markets is an implicit function of the regulation power sold to grid operators. The implicit function quantifies the amount of power that needs to be purchased to cover the expected energy loss that results from providing frequency regulation. We show how the marginal cost associated with the expected energy loss decreases with roundtrip efficiency and increases with frequency deviation dispersion. We find that the profits from frequency regulation over the lifetime of energy-constrained storage devices are roughly inversely proportional to the length of time for which regulation power must be committed.

Continuous auction mechanism model for safety electric energy market transaction

With the rapid development of microgrid in the electric power industry, the microgrid electric energy transaction has begun to be marketized, and the research on the microgrid electric energy trusted transaction has important theoretical research value and social value. The existing blockchain-based microgrid electric energy trusted transaction models mostly focus on energy management and scheduling control between microgrids when conducting electric energy transactions, and do not fully consider the bidding problems in the market-based transaction of microgrid electric energy, resulting in trading strategies are difficult to adapt to new market changes. In response to this problem, this paper proposes a reliable transaction approach for microgrid electric energy based on a continuous two-way auction mechanism. The proposed strategy accounts for the volatility of electricity prices in the microgrid trading market and employs the continuous two-way auction mechanism to evaluate the microgrid electricity trading tactics. In the microgrid electric energy transaction, the self-adaptive learning theory is applied to adjust the quotations of both parties, so that both parties can make reasonable quotations according to the market environment. By simulating experimental data, the findings indicate that the continuous two-way auction mechanism transaction strategy enables both parties to modify their quotations based on market transaction information, thereby displaying a high degree of flexibility in microgrid electricity’s market-oriented trading.

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduling of a continuous power-intensive production process in a multi-stage problem. We optimize both total annualized cost and global warming impact and consider local photovoltaic and wind electricity generation, an electric battery, and electricity trading on day-ahead and intraday market. We find that installing a battery can reduce emissions and enable large trading volumes on the electricity markets, but significantly increases cost. Economically and ecologically-optimal operation of the process and battery are driven primarily by the electricity price and grid emission factor, respectively, rather than locally generated electricity. A parameter study reveals that cost savings from the local system and flexibilizing the process behave almost additively.

Dams in the Mekong: a comprehensive database, spatiotemporal distribution, and hydropower potentials

Abstract. Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential using present-day discharge from the CaMa-Flood model (2011–2015, 0.05°) and future discharge from the WaterGAP2 model used for ISIMIP2b (2021–2040, 0.5°). The dam count of 1055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1242 MW in the 1980s to 69 199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+16 854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+18 223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1 334 683 MW, where Laos is the highest at 514 887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2 000 000 MW. Laos and China are the highest at around 900 000 MW each, together forming over 80 % of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better planning and management in the future hydropower hotspot Laos. The Mekong dam database is publicly available at https://doi.org/10.21979/N9/ACZIJN (Ang et al., 2023).

Holistic coordination of transactive energy and carbon emission right trading for heterogenous networked multi-energy microgrids: A fully distributed adaptive consensus ADMM approach

The paradigm of multi-energy microgrids (MEMGs) with internal energy sharing and trading is considered a promising option to empower low-carbon energy transitions. In this work, a holistic and efficient optimization framework is proposed to coordinate electricity, heat, and carbon emission right (CER) trading activities among multiple MEMGs. The proposed framework captures the heterogeneity of MEMGs, peer-to-peer transactive energy of different forms, and CER trading and network constraints together in a holistic fashion. To preserve the privacy of MEMGs and ensure transparency of CER trading, a fully distributed adaptive consensus alternating direction method of multipliers (AC-ADMM) is proposed, based on the combination of parallel computation capability of C-ADMM and the penalty parameters adaptive tuning mechanism. Case studies demonstrate the effectiveness of the proposed AC-ADMM method and reveal significant economic and environmental benefits for MEMGs by coordinating different forms of energy and CER trading in local transactive markets, while satisfying the network constraints. The superior convergence performance of the proposed method is also validated against conventional ADMM and C-ADMM methods.

Economic Pricing in Peer-to-Peer Electrical Trading for a Sustainable Electricity Supply Chain Industry in Thailand

The state-owned power Electricity Generating Authority of Thailand (EGAT), a monopoly market in charge of producing, distributing, and wholesaling power, is the focal point of Thailand’s electricity market. Although the government has encouraged people to install on-grid solar panels to sell electricity as producers and retail consumers, the price mechanism, i.e., purchasing price and selling prices, is still unilaterally determined by the government. Therefore, we are interested in studying the case where blockchain can be used as a free trading platform. Without involving buying or selling from the government, this research presents a model of fully traded price mechanisms. Based on the study results of the double auction system, data on buying and selling prices of electrical energy in Thailand were used as the initial data for the electricity peer-to-peer free-trading model. Then, information was obtained to analyze the trading price trends by using the law of demand and supply in addition to the principle of the bipartite graph. The price trend results agree well with those of price equilibrium equations. Therefore, we firmly believe that the model we offer can be traded in a closed system of free-trade platforms. In addition, the players in the system can help to determine the price trend that will occur according to various parameters and will cause true fairness in the sustainable electricity supply chain industry in Thailand.

Two-Stage Credit Management for Peer-to-Peer Electricity Trading in Consortium Blockchain

Two-Stage Credit Management for Peer-to-Peer Electricity Trading in Consortium Blockchain

Error state prediction of capacitor voltage transformer based on TimesNet and gate control unit

The stability and reliability of the error state of capacitor voltage transformer (CVT) are poor, which affects the fairness of electricity trade settlement and the safe operation level of power grid. However, the CVT error data is superimposed by different periodic information such as daily period, monthly period and quarterly period, which makes the prediction of transformer error state challenging. In this paper, a CVT error state prediction method based on TimesNet and gate control unit (GRU) is proposed. The TimesNet network is employed to capture the intraperiod-variation and interperiod-variation characteristics of the ratio difference data of a-phase, b-phase and c-phase, and the feature data and GRU model are employed to predict the output. The simulation results demonstrate that the mean square error (MSE) and mean absolute error (MAE) of the proposed model are 0.0002 and 0.0101, respectively, indicating that this model has lower prediction error and higher prediction accuracy.

Gas power — How much is needed on the road to carbon neutrality?

The national and international expansion of renewable energy and the retirement of conventional capacity are increasing the challenge of ensuring generation adequacy in the European power system. New gas-fired power plants have been perceived as a promising option to secure supply. However, recent geopolitical developments have raised concerns about the high dependence on natural gas. This paper explores how different scenarios of power consumption and renewable energy targets in Germany determine the reliance on gas-fired power generation capacity in 2030, 2040, and 2050. Using the electricity market model ELTRAMOD it examines the effects of the different scenario assumptions on gas-fired capacity and generation, renewable capacity, electricity trade, and greenhouse gas emissions in Germany and abroad. The results demonstrate a clear need for reliable capacity in the future, with gas-fired capacity accounting for the largest share. In all scenarios analyzed, Europe’s dependence on natural gas increases from historical levels until 2030 and then falls only slightly below historical levels until 2040. Stricter German renewable energy targets shift natural gas use from Germany to other European countries. Phasing out coal in Germany before 2030 instead of in 2038 makes Germany dependent on reliable capacity from abroad and increases its reliance on natural gas.

Is the Existing Hydropower Development Policy, 2001 Investment Friendly?

Nepal is endowed with an abundance of hydro resources. Its gross hydropower potential is estimated to be 83,290 MW technically. Out of which, 42,133 MW is ascertained as economically exploitable. However, in the span of 112 years, only around 2,900 MW is harnessed. It is obvious that the development of hydropower is a public policy-related matter. Previous studies exhibit several complications in hydropower development policy, 2001 such as private sector participation in electricity trade, lack of one window system, term of project, hedging mechanisms, foreign direct investment, and so forth. In this regard, this study aims to investigate its efficiency and effectiveness through quantitative research design. Herein, for the sake of empirical research three independent variables namely, Financial Incentives Sufficiency (FIS), Return on Investment Sufficiency (RoIS), and Market Access Sufficiency (MAS), and one dependent variable namely, Investment Friendliness (IF) was dealt. To collect the opinion of respondents a set of questionnaires comprising 11 questions was developed and approached to 108 informants from 25 diverse sectors of the hydro-industry. Herein, deductive reasoning was persuaded. For analyzing data, statistical tools such as arithmetic mean, standard deviation, factor analysis, Chi-Square tests, Mann Whitney "U" test, and Kruskal-Wallis "H" test were computed. Based on empirical analysis this study concludes that the hydropower development policy, of 2001 is investment-friendly. However, the study manifested a number of rooms that were to be addressed as changing the expectations of investors. On this ground, the findings of this study will be applicable with a view to formulating a more investment-friendly hydropower policy in the future.

A novel socio-economic-environmental model to maximize prosumer satisfaction in smart residential complexes

As the adoption of distributed and renewable energy resources helps consumers evolve into prosumers who generate and trade their own energy, optimizing distributed renewable energy resources, electric vehicles, and energy storage in smart residential complexes is crucial to enhance satisfaction and capitalize on their potential for environmental and economic benefits. Nonetheless, limited progress has been made to quantify the impact of prosumer and energy manager preferences and the integrated socio-economic-environmental dynamics on energy scheduling and stakeholder satisfaction within smart communities. This study aims to overcome this challenge by proposing a socio-economic-environmental optimization model based on a preference-oriented management approach, which quantifies the impact of energy scheduling on stakeholder satisfaction in smart communities. The model inputs include infrastructure configurations, energy consumption patterns, and weather data. The simulation method includes the application of deep learning and System Advisor Model software in predicting weather conditions and photovoltaic and wind turbine systems energy generation. The simulation results are then imported as parameters in GAMS software to optimize the stakeholder satisfaction using CPLEX solver. Quantitative results show that with the simultaneous use of electric vehicles and energy exchange between energy communities, the highest level of satisfaction of the stakeholders in the smart grid including energy communities has been achieved at hour 24 and at the rate of 0.99 per-unit. The model reveals notable enhancements in stakeholder satisfaction, particularly through the integration of electric vehicles and energy trading in smart grids. It underscores the critical impact of aligning renewable energy strategies with prosumer socio-economic-environmental preferences, offering valuable insights for energy managers, policymakers, and community planners in fostering efficient, community-oriented smart grid systems.

Transactive energy trading in distribution systems via privacy-preserving distributed coordination

This paper presents an approach to the transactive energy (TE) market that aims to improve the network efficiency and fairness of electricity market in distribution systems. The TE market is designed to allow subscribers to trade electricity in real-time based-on their energy consumption and generation forecast. The proposed framework utilizes distribution locational marginal pricing (DLMP), which accurately reflects the costs of electricity transmission and distribution, to fairly allocate these costs among subscribers. The Small Neighborhood Search Optimal Points (SNSOP) algorithm is then introduced to solve the challenge of finding optimal points for all participants in the TE market, as the maximum financial gain for one participant comes at the maximum financial loss of another participant. The results of the case study show that the proposed framework leads to a more efficient and reliable electricity network, by enabling decentralized control and reducing losses through optimal trades in the TE market.

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush–Kuhn–Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP.

Retail competition among multi-type retail electric providers in social networks

Liberalization of the electricity retail market has intensified the competition among different types of retail electricity providers (REPs), leading REPs to pay more attention to electricity consumers' (ECs') preferences for electricity service quality, such as reliability and flexibility. In addition, social learning also greatly influences consumers' electricity purchase strategies. Therefore, this paper models the price and service quality competition among multi-type REPs in a two-layer electricity retail market network that integrates electricity trading and consumer social learning. Firstly, we construct a two-layer network that transforms the dynamic process of ECs switching REPs and changing neighbors into the reconfiguration of the network structure. The upper layer is a bipartite network to express the dynamic transaction between REPs and ECs, while the lower layer is a random network to represent the learning interaction between ECs. Secondly, considering network externalities, we model a retail competition based on price and ECs' preferences for electricity service quality in the network above and get the equilibrium solutions in different scenarios by simulating the electricity consumption data of 600 London households. The results demonstrate that network externalities hinder the transmission of price signals, and the density of the EC network affects the market game result, while the update speed of the EC network has little effect. This study provides theoretical support for multi-agent decision-making in the electricity retail market.