Electricity Trading Research Articles

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

How does electricity trading affect the carbon emission reduction of electric vehicles from the perspective of electricity consumption?

How does electricity trading affect the carbon emission reduction of electric vehicles from the perspective of electricity consumption?

Aligning the Western Balkans power sectors with the European Green Deal

Abstract Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. We investigate whether hydro and non-hydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries' power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.

Buildings as Batteries

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94.

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

Applying trade mechanisms to quantify dynamic GHG emissions of electricity consumption in an open economy - The case of Switzerland

Quantifying greenhouse gas (GHG) emissions from electricity consumption presents a significant challenge due to the variable nature of the generation mix throughout the year and the complex dynamics of cross-border electricity trade. In this paper, we introduce an innovative methodology designed to accurately capture the hourly GHG emissions linked to electricity imports, and thus electricity consumption. Contrary to conventional approaches, our methodology identifies the relevant generation technologies in exporting countries which are activated for export. This selection is based on (i) the classification of the technologies according to their dispatch cost (merit order), and (ii) considers trade mechanisms from economic theory.We apply this methodology to Switzerland, a country characterized by a low-carbon electricity generation mix, which however heavily relies on imports from neighbouring countries, particularly during the winter months. Over the period from 2017 to 2021, we find that 88 % of Switzerland's domestic electricity demand is met by its own generation—primarily hydro and nuclear—while the remaining 12 % is imported, mainly from fossil-fuel-based sources. Consequently, the average annual GHG emission factor for electricity consumption in Switzerland is 98 g-CO2eq/kWh, with 29 % attributed to domestic generation and 71 % to imports. This figure is 40 % higher than the 70 g-CO2eq/kWh estimated using a conventional approach, which typically underestimates the specific impacts of electricity imports.The disparity between these approaches becomes even more pronounced when considering the seasonal and hourly variations in the electricity mix and the resulting emission factors. These insights are particularly crucial in the context of emerging electricity demands, such as heat pumps and electric vehicles, where accurate GHG accounting can significantly influence policy and investment decisions.

Carbon footprint evaluation for electric vehicles considering green electricity trading

Electric vehicles (EVs) are often seen as a symbol of environmental friendliness. However, while EVs themselves do not directly emit carbon, the electricity they rely on may still contribute to carbon emissions. It is challenging to accurately assess the environmental impact of different types of electric energy used in charging stations due to unified pricing transactions. This hinders the precise measurement of each EVs' carbon footprint. To address this issue, this paper proposes a method for evaluating the carbon footprint based on Carbon Emission Flow (CEF) and energy traceability. A carbon integral mechanism is designed based on the China Certified Emission Reduction (CCER) scheme for coupling EVs with green electricity trading. These elements are integrated into the EV carbon footprint evaluation. Furthermore, the EVs' carbon footprint is minimized to select the lowest-carbon charging path for EVs owners. The feasibility of this proposed method is verified using an example from IEEE33 which quantifies EV footprints and indirect carbon emissions within the distribution network, ultimately encouraging consumption of green electricity by EVs and promoting more environmentally friendly modes of travel.

Spatial national multi-period long-term energy and carbon planning scenarios in Ecuador’s electric system

The Republic of Ecuador is developing a comprehensive plan to meet the increasing residential, industrial, and commercial energy demands. With a population of 17.08 million in 2018, the country is experiencing an annual average energy growth of approximately 7.13% until 2027. This research aims to model the incorporation of new and proposed power plants strategically planned to cater to the expanding demand needs. Ensuring that our existing energy infrastructure can adequately meet the current and growing needs of the residential, commercial, and industrial sectors should be made possible. In this paper, we present several hypotheses that need to be made to identify the potential demands for residential, industrial, and commercial requirements, making it more accessible to build new facilities that use renewable energy. Traditional and unconventional renewable energy sources, such as hydro-power, wind, and solar power, are being explored to generate electricity. The research employs quantitative methodology, beginning with gathering information and a detailed data analysis. Subsequently, a scenario-based model will examine the impact on energy demand and supply from 2020 to 2035. The results show that the Santiago hydroelectric project is becoming essential for the country’s energy development and that the current national power system will only be able to supply the electricity demand when the industrial city of Posorja is being developed. We suggest that to maintain Ecuador’s electricity trade balance as an electricity exporter, we continue with the energy investment plans currently issued by the Ministry of Energy and Mines. This study is relevant for establishing global financing arrangements involving the public and private sectors. In doing so, we can meet our hypothetical scenarios, ensuring that all energy needs are met by 2035. This will involve developing a robust national grid system with sufficient reserve capacity, meeting residential and projected commercial and industrial demand.

THE RELATIONSHIP BETWEEN ELECTRICITY CONSUMPTION, TRADE, AND ECONOMIC GROWTH IN NIGERIA

The study examines the relationship between electricity consumption, trade, and economic growth in Nigeria. The results show that increased electricity consumption positively impacts economic growth in both the short and long run. Foreign direct investment (FDI) also has a positive and significant effect, indicating that higher FDI leads to long-term economic growth. Trade openness was found to contribute to environmental growth. The study recommends that policymakers strengthen the manufacturing sector to better harness the benefits of trade openness and reduce its negative impacts

Electricity trade at exchanges of the world: Contextual analysis of Indian electricity exchanges

This study probes the relationship between price and traded volume in electricity exchanges worldwide. Deploying the generalised method of moments estimation on panel data from the day-ahead market of exchanges in 20 countries for the period 2015–2020, this study finds that an increase in the share of exchange-traded electricity in a country's total electricity consumption significantly reduces price. A critical analysis of the Indian electricity exchanges in this study reveals that even after 13 years of operations, consumers do not derive surplus as is expected from commodity exchanges.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Short-term electricity price forecasting through demand and renewable generation prediction

Electricity market prices depend on various variables, including energy demand, weather conditions, gas prices, renewable generation, and other factors. Fluctuating prices are a common characteristic of electricity markets, making electricity price forecasting a complex process where predicting different variables is crucial. This paper introduces a hybrid forecasting model developed for the Spanish case. The model comprises four forecasting tools, with three of them relying on artificial neural networks, while the demand forecasting model employs a similar-day approach with temperature correction. This model can be employed by electrical energy trading companies to enhance their trading strategies in the day-ahead market and in derivative markets with a time horizon ranging from two to ten days. The results indicate that, with a forecasting horizon of two days, the price forecast has a rMAE of 8.18%. Furthermore, the model enables a market agent to accurately decide whether to purchase energy in the daily market or in the derivatives market in 69.9% of the days.

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Research on cooperative operation optimization method of internet data center alliance considering diversified flexibility

With the rapid development of large-scale internet data center in China, the goal of carbon peak and carbon neutrality has put forward higher requirements for low-carbon economic operation of internet data center. Considering complementary advantages among internet data centers, this paper proposes a cooperative operation optimization method of internet data center alliance based on diversified flexibility. Firstly, a cooperative operation structure of alliance considering electricity and carbon trading between members was built, as well as the diversified flexibility model considering carbon capture and power to gas device. Then, a cooperative optimization operation model of alliance was constructed based on Nash bargaining theory, and the allocation of cooperation benefit among members can be realized based on electrical carbon contribution rate. In order to improve solution convergence, the alternating direction multiplier method is improved by penalty factor updating and correction factor modeling method. Finally, the proposed method is applied to the demonstration project of “East Data and West Computing” in Qingyang, Gansu, China, and the trial run results are consistent with the theory analysis. On the one hand, the diversified flexibility and the electricity and carbon trading between members can effectively reduce the total cost and carbon emissions of alliance. On the other hand, the cooperation benefits distribution strategy based on the electrical carbon contribution rate can effectively enhance enthusiasm of members to participate in alliance. The improved alternating direction multiplier method can achieve distributed optimization of alliance, protect the privacy of each member, and has good convergence and scalability.

Has Real Time Spot Electricity Market in India Impacted Day-Ahead Spot Electricity Market?

Introduction of Real-Time Market (RTM) on June 1st, 2020, marked pivotal advancement in electricity trading in India featuring bi-hourly auctions and power transmission based on temporal blocks. Adherence to operational protocols outlined in Procedure for Scheduling Collective Transactions in RTM along with statutory directives like CERC Power Market Regulations 2010 ensures efficient market operation. Notable features include 15-minute contract trading, clandestine bidding and authorization from SLDC. Day-Ahead Market (DAM) facilitates electricity exchange through double-sided closed auctions ensuring compliance with regulatory protocols. In this study we explore whether Real Time Spot Electricity Market in India has impacted Day-Ahead Spot Electricity Market. Findings of the study provide crucial insights for market participants, stakeholders, open access consumers and electric utilities is India.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

Effect Analysis of Covid-19 on China’s Capital Market for Property-Rights Exchange Based on Functional Clustering

In August 2015, the State Council of the People’s Republic of China listed China’s capital market for property-rights exchange as the capital market along with the Chinese stock market. Consequently, China’s capital market for property-rights exchange is an integral part of the country’s multilayered capital market. The COVID-19 pandemic has had significant effects on the capital markets. Since China’s capital market for property-rights exchange mainly serves nonlisted companies, it is complicated to obtain its exchange data to perform COVID-19’s effect analysis. This study investigates COVID-19’s effect on China’s capital market for property-rights exchange. In this paper, we collected the online property rights-exchange data from 2017 to 2020 of an electric trading platform. The functional principal component analysis method is innovatively introduced to explore the online property rights-exchange fluctuation to get an insight into COVID-19’s effect on China’s capital market for property-rights exchange. Based on the principal component scores, COVID-19’s effect on different property rights-exchange institutions are divided into two categories using principal component clustering. A time-series model is used to quantify the effects in the two categories. The research results finally show that online property rights-exchange can reduce the negative effect of COVID-19 on China’s capital market for property-rights exchange, which encourages property rights-exchange institutions to accelerate the use of the online platform to overcome the challenges of the pandemic.