Trading power instead of energy in day-ahead electricity markets

Philipsen, Rens; Morales-España, Germán; Weerdt, Mathijs de; Vries, Laurens de

doi:10.1016/j.apenergy.2018.09.205

Cited by 29 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the articles [354][355][356][357][358][359] have adopted the TOU as a price modality, whereas the References [360][361][362][363][364][365][366][367][368][369][370] have used the RTP scheme. Furthermore, CPP modality can be efficiently utilized, as reported in References [371][372][373][374][375][376][377], while the authors of References [378][379][380][381][382][383] have proposed a DAP as a pricing modality in a competitive electricity market. However, the complexity and cost issues may constitute the main barriers toward the expansion in adopting different pricing frameworks as a day-to-day tool in the existing grid [384,385].…”

Section: Dynamic Pricing Mechanismsmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

et al. 2020

View full text Add to dashboard Cite

The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

show abstract

Section: Dynamic Pricing Mechanismsmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The hourly spinning reserve requirement is determined in [32] and is guaranteed by Equations (22) and (23). The parameter sr d t,s represents the expected inter-hourly increase in power demand between t and t +1 and sr w t,s represents the most likely wind power loss calculated from historical data.…”

Section: Power System Characteristicsmentioning

confidence: 99%

Unit commitment in a hybrid diesel/wind/pumped‐storage isolated power system considering the net demand intra‐hourly variability

Fernández-Muñoz

Pérez‐Díaz

2021

IET Renewable Power Gen

View full text Add to dashboard Cite

This paper presents a novel two-stage stochastic mixed-integer linear programming model for the generation scheduling of a hybrid diesel/wind/pumped-storage power system that considers the intra-hourly net demand variability during the first stage. The model aims to minimise the scheduling cost, calculates the power system storage opportunity cost and considers that the start-up cost of thermal units depends on the time passed since the previous shutdown in a detailed manner for the first stage (i.e. using integer variables) and in an approximate manner (relaxing the integrality constraints) for the second stage. To increase the model's computational efficiency, a solution approach based on the Benders decomposition technique is applied. The model is implemented in the real power system of El Hierro island, an isolated hybrid diesel/wind/pumped-storage power system with a closed-loop pumped-storage plant and high wind power installed capacity. The schedules provided by the proposed model are compared to the ones provided by a similar model that does not consider the intra-hourly net demand variability, resulting in generation schedules with a significantly lower wind energy curtailment. The results demonstrate the importance of considering the intra-hourly net demand variability in order to integrate more wind energy at a moderate extra cost.

show abstract

“…Participants in various electricity markets are faced with complex decisions, with long-and medium-term implications that cannot be anticipated, both due to the complex business rules, as well as due to the large number of participants and transactions. Aspects regarding the formalization of business rules related to day-ahead markets, which encourage momentary balance between supply and demand, can be found in [33]. Also, a realistic simulator of the DAM of Spain, where all the rules that govern this market are modeled, was proposed by [34].…”

Section: Key Performance Indicators (Kpis) Business Rules and Mcdm Mmentioning

confidence: 99%

A Trading Simulator Model for the Wholesale Electricity Market

Oprea

Bârã

Preoțescu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Currently, to meet the requirements of modern power systems as fully and efficiently as possible, the electricity markets have diversified greatly. Under these conditions, it becomes difficult for a producer to determine the structure of transactions that is financially optimal. Starting from the operational rules of the power systems that have shaped the electricity markets structure, the objective of this paper is to develop an electricity market simulator model that includes the basics of a best practice guide for producers that compete on various electricity markets to carry out the trading activities and enhance their financial results. The market simulator model considers both the bilateral long-or mid-term agreements and short-term offers on day-ahead, ancillary services and balancing markets providing the entire trading scenario and associated cash-flow and risks. Its significance consists in assisting the producer to plan its resources and create projections by performing multiple trading scenarios and selecting the best one. Thus, this paper proposes to uncover constraints and business rules for a simulator model assisting the market players to access the electricity markets and select the best option using Multiple-Criterial Decision-Making (MCDM) methods (Electre, Topsis, Analytical Hierarchy Process) or the weighted Euclidean distance. The simulations comprise four trading scenarios for different types of producers (gas or fossil-powered generators) generating 100 MW, that are ordered by independent criteria. The results obtained with MCDM and the proposed method showed that they indicated the same scenario as the best trading option based on the type of the producer. INDEX TERMS electricity market trading simulator, cost/revenue allocation, transaction risk.

show abstract

Trading power instead of energy in day-ahead electricity markets

Cited by 29 publications

References 19 publications

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

Unit commitment in a hybrid diesel/wind/pumped‐storage isolated power system considering the net demand intra‐hourly variability

A Trading Simulator Model for the Wholesale Electricity Market

Contact Info

Product

Resources

About