Electricity Trading Research Articles

Seasonality and trade in hydro-heavy electricity markets: A case study with the West Africa Power Pool (WAPP)

Hydroelectric power plants account for 25% of West Africa's total installed electricity generation capacity. In general, electricity generated by these plants is seasonal, and intra-annual fluctuationshave a considerable impact on electricity supply and cross-border electricity trade. This study soft-links a global hydrologic model to a multi-region capacity expansion and planning model for the West Africa Power Pool (WAPP) to examine the effects of seasonality in hydropower electricity generation on electricity trade as well as the economic benefits of unconstrainedcross-border electricity trade. Transitioning from a rainy to a dry season decreases hydroelectricity generation by an average of 40% in a typical year. Between 2018 and 2050, satisfying the region's electricity demand will require significant capital investment; nevertheless, extending from current bilateral electricity trade agreements to a fully competitive cross-border electricitytrade will result in net annual savings of $3 billion in 2015 dollars. To achieve year-round energy security, countries should plan to diversify their energy sources to ensure that supply reliability can be maintained in the event of lower supply during the dry season. This study represents a novel examination of seasonality impacts on hydropower modeling and may serve as a test case to inform future modeling efforts in other regions.

Price-Guided Peer-To-Peer Trading Scheme and Its Effects on Transaction Costs and Network Losses

Distributed energy resources (DERs), such as small-scale renewable energy generators, storage systems, and controllable loads, have been attracting great attention. Accordingly, interest in peer-to-peer (P2P) energy trading between prosumers with DERs is growing. The prosumers may perform the P2P electricity trading within the loss-guided framework, where network losses are primarily considered during the peer matching process. However, the loss-guided framework has limitations in that prosumer welfare is neglected in favor of prioritizing the network losses caused by the P2P transactions. Thus, in this study, a price-based framework for P2P electricity trading is suggested, where the prosumer welfare is considered by including not only network loss costs but also energy costs in the matching procedure. The effects of the suggested price-based framework on network efficiency, prosumer welfare, and social welfare are examined by comparing simulation results with the loss-guided framework and the random transactions. Further, how those three properties are affected by the change in loss price is analyzed and a guideline for the suitable choice of the loss price is suggested.

Total supply capability model for distribution network with electric trading market

The electricity trading market promotes the frequency and scale of power exchange in the distribution network, the uncertainty of trading volume, trading duration, and trading partners affect the total supply capability (TSC) of the distribution network (DN). The paper proposed a TSC model of DN with electric trading market. Firstly, the load clearing model of electricity trading is established. Then, the least square method is used to fit the growth curve functions of conventional load and power trading load. TSC model of DN is developed based on the growth functions of two loads. Finally, the TSC model is solved by the improved repetitive power flow algorithm (RPA). The simulation results of the improved IEEE 54-bus test system verify the feasibility and applicability of the proposed TSC model.

Shared PV Systems in Multi-Scaled Communities

In past years, Sweden has been facing a rapid growth of photovoltaic cells, and the total PV installation capacity increased from 300 kW to 1090 MW (2006–2020). The increased number of PV users was a result of active support from the Swedish government with an aim of achieving multiple sustainable goals regarding renewable energy. This project evaluates the profitability of shared PV systems in communities of different sizes in Sweden. This study aimed to contribute to the literature by filling the research gap of presenting the financial benefits at different community scales. The electricity use profiles consisted of hourly measured electricity use that was derived from 1067 individual Swedish apartments. The profiles were then used to create multi-scaled communities with shared PV systems. The mid-market price model was implemented to simulate electricity trading among prosumers in the community using Visual Basic Applications (VBA) in MS Excel. Further, the electricity costs were used for Life Cycle Cost (LCC) assessment. To demonstrate the increase in profitability, the LCC results of households with shared PV systems were compared to households that own PV individually and households that do not own a PV system. The evaluation showed the financial benefits of shared PV systems in comparison with individually owned PV systems. This study also demonstrated the increase in profitability and the reduction in payback time for the average household if sharing a PV system as part of a larger community.

Impact of the Two-Tier System on Learning of Physics Concepts: A Case of Electrical Trade Skill and Circuits

This study investigated the influence in form of attitude, performance, interest to those learners taking electrical engineering at trade test level, on their ability to understand electrical circuits a physics topic in the science 5124 curriculum. The learners were purposefully selected from Solwezi School for Continuing Education (SSCE) and the research was a descriptive design. The results were analysed using a descriptive and inferential statistics including the SPSS software for research analysis. A pilot study was conducted, instruments adjusted, and survey implemented, instructional model designed and evaluated for its effectiveness. Data was collected using questionnaires, test results analysis and interviews. The reliability of the instruments was tested using Cronbach’s alpha (α). The findings of the research pointed to the fact that, when well-coordinated, learning electrical engineering under the two-tier system, improves pupils’ academic performance in the subject of physics. It is hoped that this study will help to scientifically legitimize the benefits of the two-tier system which the ZECF 2013 formulated into the new curriculum of 2013.

A Preference Analysis for a Peer-to-Peer (P2P) Electricity Trading Platform in South Korea

The Korean government is committed to advance the country’s energy transition to greener energy by increasing the share of renewable electricity to 20 percent by 2030 and to 30–35 percent by 2040. Achieving these goals will necessitate flexibility in energy markets as well as innovative business models and technological solutions. Peer-to-peer (P2P) electricity trading platforms (ETPs) are rapidly gaining traction, as they enable the integration of distributed energy sources into power systems. This study explores whether South Koreans are willing to participate in a hypothetical P2P ETP, which allows prosumers (who both consume and produce electricity) to trade electricity surpluses with their neighbours or with KEPCO (Korea Electric Power Corporation). We employ a choice experiment (CE) to examine respondent heterogeneous preferences and their willingness to pay (WTP) for a hypothetical P2P ETP in South Korea. Our findings indicate that two-thirds of total respondents in our CE are in favour of a P2P ETP if available. Cost savings and security play an essential role in respondent preferences for a P2P ETP business model. Respondents also prefer to trade renewable electricity with KEPCO rather than with other individuals. Per our statistical estimations, respondent WTP for a P2P ETP was estimated at USD 55.68/per month. Our analysis strongly recommends increasing consumer awareness of P2P ETPs to spur adoption. Energy trading platforms that are anchored in secure block chain technology can generate cost savings as well as support the country’s policy tilt toward green energy.

A stochastic approach to dynamic participation in energy communities

With energy communities and local electricity markets on the rise, the possibilities for prosumers to be actively involved in the energy system increase, creating more complex settings for energy communities. This paper addresses the following research question: Does having knowledge about the future development in energy communities help make better decisions selecting new participants than without consideration of any future developments? Each year, the community is faced with the exit of existing members and a portfolio of possible new entrants with different characteristics. For this purpose, a bi-level optimization model for dynamic participation in local energy communities with peer-to-peer electricity trading, which is able to select the most suitable new entrants based on the preferences of the members of the original community, is extended to a stochastic dynamic program. The community wants to plan a few years ahead, which includes the following uncertainties: (i) which members leave after each period, and (ii) which are the potential new members willing to join the community. This paper’s contribution is a stochastic optimization approach to evaluate possible future developments and scenarios. The focus lies on the contractual design between the energy community and new entrants; the model calculates the duration of contracts endogenously. The results show a sample energy community’s decision-making process over a horizon of several years, comparing the stochastic approach with a simple deterministic alternative solution.

Implications of intercontinental renewable electricity trade for energy systems and emissions

A rapid global energy transition, including the ramping up of electricity generation from renewables, is needed to limit global warming to 2 °C or 1.5 °C. However, renewable resource endowments vary widely between regions, and renewable electricity is currently mainly used locally. Here we use a global integrated assessment model to explore the implications of renewable electricity trade via a set of planned direct-current-type ultra-high-voltage (UHVDC) transmission lines for global energy transition and climate change. We find that renewable electricity trade across large world regions via the underlying UHVDC interconnection can boost renewable electricity production and reduce 2020–2100 cumulative CO2 emissions from the power sector up to 9.8%. Financial investments in the UHVDC lines are offset in the long term by reduced investments in other electricity-generation options, including nuclear and storage. Finally, we find that renewable electricity trade can substantially reduce air pollutant emissions in importing regions. Projects are under way for direct-current ultra-high-voltage transmission lines that would allow trading of renewable electricity across world regions. Guo et al. use integrated assessment models to explore different scenarios for the operation of these projects and assess their potential for decarbonization.

Electricity Markets in the Context of Distributed Energy Resources and Demand Response Programs: Main Developments and Challenges Based on a Systematic Literature Review

Distributed energy resources have been increasingly integrated into electrical grids. Consequently, electricity markets are expected to undergo changes and become more complex. However, while there are many scientific publications on the topic, a broader discussion is still necessary. Therefore, a systematic literature review on electricity markets in the context of distributed energy resources integration was conducted in this paper to present in-depth discussions on the topic, along with shedding light on current perspectives, the most relevant sources, authors, papers, countries, metrics, and indexes. The software R and its open-source tool Bibliometrix were used to perform the systematic literature review based on the widely recognized databases Web of Science and Scopus, which led to a total of 1685 articles after removing duplicates. The results demonstrate that demand response, renewable energy, uncertainty, optimization, and smart grid are the most-used keywords. By assessing highly impactful articles on the theme, emphasis on energy storage systems becomes clear compared to distributed generation and electric vehicles. However, electric vehicles draw attention in terms of citations. Furthermore, multi-level stochastic programming is the most-applied methodology among highly impactful articles. Due to the relevance of the demand response keyword, this paper also conducts a specific review on the topic aligned with electricity markets and distributed energy resources (296 articles). The results demonstrate that virtually all high-impact publications on the topic address day-ahead or real-time pricing. Based on the literature found, this paper presents a discussion on the main challenges and future perspectives related to the field. The complexity of electrical power systems and electricity markets is increasing substantially according to what this study found. Distributed generation development is already advanced, while energy storage systems and electric vehicles are limited in many countries. Peer-to-peer electricity trading and virtual power plant are newer concepts that are currently incipient, and DR programs showcase an intermediate stage of evolution. A particular lack of research on social issues is verified, and also a lack of all-encompassing studies that address multiple interconnected topics, which should be better addressed in the future. The in-depth assessment carried out in this paper is expected to be of high value to researchers and policy-makers and facilitate future research on the topic.

Secure distributed power trading protocol for networked microgrids based on blockchain and elliptic curve cryptography

AbstractThe definition of an electricity trading protocol among microgrids is not an easy task to perform due to the grid's increasing requirements. In fact, a trading process must provide security and especially privacy of the buyer and seller identities as well as prices. Also, guaranteeing a financial profit to trading parties is of great importnace. Moreover, matching the full distributed paradigm is required since it will be executed in a distributed system. All these constraints must be satisfied with consideration to the computational costs especially data storage. In this paper, an electricity trading protocol is introduced to ensure: (1) the security and privacy of buyers, sellers, and sales information, (2) the trading process distribution, and (3) the minimisation of the stored data. The protocol builds upon the blockchain technology and the Elliptic Curve Cryptography. To achieve the protocol goals, a new architecture based on a concept of membership of microgrids to families and groups of families is defined. The proposed protocol is implemented using Java programming langauge. An honest‐but‐curious‐adversary security model is applied to a formal case study to prove security of the protocol.

Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Trading algorithms to represent the wholesale market of energy communities in Norway and England

The development of local electricity markets (LEMs) and energy communities is accelerating the shift from consumerism to prosumerism. However, there is no concrete understanding of how electricity sharing in LEMs should be organized, a local wholesale market within or centralized sharing? This paper explores trading algorithms that can represent a competitive market and bidding conditions within a LEM. That is, how well trading algorithms can represent the wholesale market of an energy community?; What is a fair LEM reference price to create bidding simulations? How do the system characteristics affect the outcome of the trading algorithms? We address these questions by analyzing a community (residential buildings) in Steinkjer (Norway) and London (UK), including PV systems and wind turbines. We first determine bids and offers based on different bidding simulations and develop a market reference price. Afterward, we applied the trading algorithms Peer-to-Peer (P2P) and Multi-unit-Double-Auction (MUDA) for local electricity trading. We compared the results in selected KPIs such as self-sufficiency, traded energy, and curtailment. We find that P2P provides a more economically efficient trading algorithm than MUDA as it generally enables more trading and thus lowers grid imports. However, there are concerns that P2P brings disadvantages such as unfair trading.

Multipler Effect in East Java (Input-Output Analysis Approach)

This study aims to examine the fundamentals of the economy of East Java with an Interregional Input-Output analysis approach. Produce 5 leading sectors (BL 1 FL 1) namely manufacturing industry, electricity supply, gas, transportation and warehousing, information and communication and company services. Changes in final demand (Household consumption, LNPRT consumption, Government Consumption, Gross Fixed Capital Formation, Inventory Change, Overseas Exports, Exports Between Provinces) indicate that the processing industry is very dominant in experiencing changes. The multiplier impact 1) output is dominated by electricity, gas 2) income is dominated by the Defense Administration and Mandatory Social Security 3) Employment is dominated by agriculture, forestry and fisheries. The Impact of Spread including manufacture, procurement of electricity, gas, construction, transportation and warehousing, provision of accommodation and food and drink, information and communication, company services, health services, and social activities. The Degree of Sensitivity including agriculture, forestry and fisheries, manufacture, procurement of electricity, gas, wholesale and retail trade, car and motorcycle repair, transportation and warehousing, information and communication, company services.

A global super-grid: sociotechnical drivers and barriers

BackgroundOne way to design an electricity system wholly based on renewables is referred to as the global Super-grid, a vision of a transmission network of unprecedented geographical scope that uses advanced technology to balance spatially and temporally varying supply and demand across the globe. While proponents, since the 1960s, have argued that a global Super-grid is technologically possible and socially desirable, and significant technical progress has been made since the 1990s, development is slow with new transmission lines being built predominantly with established technology and within the boundaries of single countries. The aim of this study is to explore sociotechnical drivers and barriers of global Super-grid development.ResultsA main driver is the century old ideas that larger grids are more efficient and contribute to cooperation and peace. Over the last decades, the level of technical knowledge and networks of proponent have grown. The Super-grid also benefits from the potential opportunity of building on existing grids. Barriers stem from the scale of investments needed to experiment, path dependences in established industry and competition from novel smaller scale solutions based on local production, energy storage and smart grid technology. Other barriers originate in the organisational and institutional complexities of international electricity trade, and in the lack of trust at local and global levels, which hinder the development of necessary coordination.ConclusionsThe analysis suggests that if the Super-grid is to become part of a future electricity system, the discourse needs to open up, move beyond simplistic ideas of efficiency and ‘technocratic internationalism’, and take into account a broader set of social benefits, risks and trade-offs.

Hydrogen-Based Networked Microgrids Planning Through Two-Stage Stochastic Programming With Mixed-Integer Conic Recourse

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications.

A two-phase market clearing framework for inter-provincial electricity trading in Chinese power grids

To fully utilize the energy resources, inter-provincial electricity trading have been conducted in multiple Chinese provincial power grids. This paper proposes a two-phase market clearing framework to facilitate inter-provincial electricity trading in Chinese power grids. In Phase one, the pre-processing of bid curves is implemented to address the multi-segment bid behaviors of massive market participants. As the pre-processing methods, network partition, curve aggregation and curve approximation are developed to reduce the computation complexity. In Phase two, a bi-level model is established to depict the market clearing process, which includes the upper-level model for inter-provincial trading and the lower-level model for intra-provincial trading. Those two models are iteratively solved by passing the optimal electricity purchase price and electricity demands to each other. Numerical experiments based on the IEEE systems and the practical Chinese inter-provincial power grids are carried out to verify the validity and scalability of the proposed framework. The simulation results indicate that: 1) the pre-processing of bid curves effectively reduce the computational burdens; 2) the inter-provincial market promotes the utilization of energy resources and increases the total social welfare. Further, the errors caused by the pre-processing methods are analyzed and the availability of the proposed framework is discussed.

Peer-to-Peer electricity trading of interconnected flexible distribution networks based on Non-Cooperative games

With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP.

Are renewable energy sources more evenly distributed than fossil fuels?

The energy transition literature assumes that renewable energy sources are more evenly distributed globally than fossil fuels. This assumption implies that the shift from fossil fuels to renewables will enable more countries to pursue energy self-sufficiency and end their dependence on imported energy. However, if the assumption is wrong, the energy transition will depend on transboundary electricity or hydrogen trade, creating new international relationships and opportunities for both cooperation and conflict. The contribution of this study is to test the assumption of the even distribution of renewable energy resources on a quantitative empirical basis. Lorenz curves are compared and Gini coefficients calculated for three types of fossil fuels and three types of renewable energy in 161 countries. The study concludes that renewable energy indeed more evenly distributed than fossil fuels. This finding lends support to claims that energy transition will bring about a more decentralized global energy system centered on prosumer countries with few long-distance energy relationships. However, the difference between the evenness of the distribution of renewable energy resources and that of fossil fuel reserves is not as great as the literature assumes. International trade in energy, and by extension international energy politics, will not disappear entirely.

Decision model for cross-border electricity trade considering renewable energy sources

Substantial benefits can be achieved from electricity trade through the interconnection of electricity networks of neighboring countries. In this study, we introduce a regional electricity exchange model to obtain an optimal power trade level between electrically-interconnected countries. In addition, producing clean electricity is one of the primary goals of many countries. Moreover, electricity production can be optimized in some areas using renewable sources and exported to regions with high demand. Thus, the power exchange using the proposed method and framework is investigated. Additionally, a decision model for planning an optimal configuration of hybrid wind/solar power systems and optimal electricity trade is presented. We analyze the economic, engineering, management, and policy issues to facilitate optimization of power generation and trade at the country level and quantify the gains from the increased trade. The proposed decision model is employed to plan electricity trade considering hybrid wind and solar energy resources. Furthermore, the effectiveness of reducing the variability of hourly power supply is evaluated by integrating the wind and solar power generation technologies. The main decision variables are the power trade value and number of installed wind turbines and solar systems. In case studies, the computational results using the proposed model yielded an optimal power trade between Iran and its neighboring countries, including Turkey, Turkmenistan, Afghanistan, Pakistan, Azerbaijan, Armenia, and Iraq. Consequently, Iran can export a maximum power capacity of 5,300 MW to its neighbors, where Iraq receives the highest share of 2,000 MW. When the electricity demand in Iran is high, Turkey, Turkmenistan, Azerbaijan, and Armenia can export 1,200, 1,000, 80, and 100 MW of electricity to Iran, respectively. Finally, various configurations of a hybrid wind/solar power generation system considering various cost and power supply variations are presented to partly supply the load requirements of the traded electricity. Consequently, Iran can supply 20% of the total demand of the electricity trade by wind and solar system installed capacities of 316.5 and 267 MW, respectively.

ANALYSIS OF THE OCCURRENCE OF THE NEGATIVE PRICE ON THE EUROPEAN ENERGY EXCHANGE (EEX) ELECTRICITY SPOT MARKET

The EEX (European Energy Exchange) has become, along with Nord Pool in Nordic countries, the most significant energy exchange in continental Europe. EEX’s prices and possibilities often represent a reference in the electricity trade on the local or regional markets of European countries. The work also shows and deliberates on the occurrence of negative prices on the EEX electricity spot market at the end of December 2008. The negative price, when predicted and allowed in the market model structure at the energy exchange, mirrors the situation on the market in which the offer has significantly exceeded the demand. Although the achievement of the negative price on the electricity market is more of an exception than a rule, it is interesting to contemplate the reasons for such an unusual occurrence. Since its introduction onto the EEX, the negative market price has been achieved only a few times, mostly in particular hours at night when the consumption of electrical energy is normally reduced. The situation analysed in this work is interesting because the negative price was achieved in the period of a few successive hours and because of the achieved amounts of the negative price, so the work contemplates on possible causes which led to such a situation. Since European electrical power systems are well connected, an analysis has been performed and the work presents how the observed occurrences in western and middle Europe have impacted electricity flows in the Croatian electrical power system and its surrounding.