Investment and Bidding Strategies for Optimal Transmission Management Dynamics: The Italian Case

Persio, Luca Di; Fraccarolo, Nicola

doi:10.3390/en16165950

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…In the electricity market environment, the most direct way to eliminate congestion is to use a transmission congestion management mechanism to adjust or cut all kinds of transactions and rearrange the power generation plan [1][2][3][4][5][6]. Considering the influence of uncertain factors in the power system, the burden of generator rescheduling resources under transmission congestion is reduced to ensure the safe and economic operation of the power system.…”

Section: Introductionmentioning

confidence: 99%

A Transmission and Distribution Cooperative Congestion Scheduling Strategy Based on Flexible Load Dynamic Compensation Prices

Sun,

Jin,

Gao

et al. 2024

Energies

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With the demand response and the massive access of distributed energy to the distribution network, it is possible to solve the transmission congestion problem by coordinating the controllable resources in a transmission network and distribution network. Aiming at resolving the problems of scattered side response resources and difficult-to-negotiate compensation prices, a bi-level optimal congestion scheduling strategy based on flexible load dynamic compensation prices is proposed. Under this strategy, the transmission network layer aims at minimizing the congestion cost and optimizes the adjustment scheme of the generator set and the node price. The active distribution network layer obtains the dynamic compensation price of the flexible load of the distribution network through the load characteristics and the node price. Through the interaction and coordination between the two layers, an optimal congestion scheduling scheme is obtained, and the transmission and distribution jointly solve the congestion problem. Based on the modified IEEE-39 experimental system, the effectiveness of the proposed strategy is verified via a simulation.

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Section: Introductionmentioning

confidence: 99%

A Transmission and Distribution Cooperative Congestion Scheduling Strategy Based on Flexible Load Dynamic Compensation Prices

Sun,

Jin,

Gao

et al. 2024

Energies

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Micro Gas Turbines in the Global Energy Landscape: Bridging the Techno-Economic Gap with Comparative and Adaptive Insights from Internal Combustion Engines and Renewable Energy Sources

Weerakoon,

Assadi

2024

Energies

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This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition.

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Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks

Di Persio,

Fraccarolo,

Veronese

2024

Mathematics

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This paper focuses on developing a predictive model for wind energy production in Italy, aligning with the ambitious goals of the European Green Deal. In particular, by utilising real data from the SUD (South) Italian electricity zone over seven years, the model employs stochastic differential equations driven by (fractional) Brownian motion-based dynamic and generative adversarial networks to forecast wind energy production up to one week ahead accurately. Numerical simulations demonstrate the model’s effectiveness in capturing the complexities of wind energy prediction.

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Investment and Bidding Strategies for Optimal Transmission Management Dynamics: The Italian Case

Cited by 3 publications

References 40 publications

A Transmission and Distribution Cooperative Congestion Scheduling Strategy Based on Flexible Load Dynamic Compensation Prices

A Transmission and Distribution Cooperative Congestion Scheduling Strategy Based on Flexible Load Dynamic Compensation Prices

Micro Gas Turbines in the Global Energy Landscape: Bridging the Techno-Economic Gap with Comparative and Adaptive Insights from Internal Combustion Engines and Renewable Energy Sources

Wind Energy Production in Italy: A Forecasting Approach Based on Fractional Brownian Motion and Generative Adversarial Networks

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