A risk-constrained Energy Reallocation Mechanism for renewable sources with a Marginal Benefit approach

Valenzuela, Paula; Lima, Delberis A.; Granville, S.

doi:10.1016/j.epsr.2018.01.016

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…As the Brazilian power system is interconnected in almost all of the country, encompassing different geographical areas with different hydrological regimes, a compulsory mechanism was created to share the lack of inflow in a certain area, that might be compensated with high inflow in another area. This mechanism is called the energy reallocation mechanism (ERM) [16].…”

Section: The Short-term Settlement and Hydro Hedge Mechanismmentioning

confidence: 99%

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Santos

Lobato

Vieira³

et al. 2022

Energies

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This paper presents a Nash equilibrium approach to model a non-cooperative game that takes place among Brazilian hydro-generating companies in the annual process called “seasonalization”. We have “remade” the seasonalizations that occurred in the period of 2013–2020 by using our model and comparing the financial outcomes with the current ones that resulted from the seasonalizations the generators made each year, with financial improvements in almost every year. By using the Nash equilibrium approach, it is possible to achieve optimal decisions concerning the seasonalization process that were not evident by using traditional methods. This approach is useful for companies willing to enhance their income and to improve their risk management by making better choices in seasonalization.

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Section: The Short-term Settlement and Hydro Hedge Mechanismmentioning

confidence: 99%

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Santos

Lobato

Vieira³

et al. 2022

Energies

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“…Through the Dirichlet model and risk registration form, the risk assessment of the energy system is realised. The error of the assessment results obtained by this method is large, and the assessment accuracy is low (Valenzuela et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Research on risk assessment method of energy system based on data mining

Zhang¹,

Chen²,

Zheng³

2022

IJGEI

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In order to overcome the problem of data index confusion and index weight ambiguity in the traditional energy system risk assessment process, this paper proposes an energy system risk assessment method based on data mining. This method USES data mining technology and quantitative index processing method to select risk assessment index of energy system, construct risk assessment index system of energy system, determine the weight of risk assessment index of energy system, and build risk assessment model of energy system on this basis. The experimental results show that the weighing accuracy and evaluation accuracy of the proposed method are above 90%, and the skewness coefficient is always close to 0. The method has a high degree of rationality in energy risk index selection, high precision in index weight and high accuracy in evaluation results, which can effectively guarantee the safety of urban energy system.

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“…However, forward contracts bring generation obligations, where the net result between energy production and the contracted amount is settled through the spot market. This can be an important and concerning issue for renewable sources such as wind, solar, and hydro generation owing to their seasonality characteristics and stochastic production behavior [4][5][6]. On the other hand, having a part of their firm energy not associated with long-term contracts can provide huge financial returns for generators in case of spot price spikes.…”

Section: Introductionmentioning

confidence: 99%

“…As a mechanism to hedge against hydrological risk, in 1998, the Brazilian electricity sector introduced the Energy Reallocation Mechanism (ERM), where the energy production from hydro generators in several regions of Brazil is unified into a portfolio to combine their complementarities and obtain a more stable cash flow [5]. In this framework, each hydro generator receives its energy from ERM each month, according to its quota participation, which is related to its Firm Energy Certificate (FEC) monthly allocation and the combined energy generation of all ERM participants.…”

Section: Introductionmentioning

confidence: 99%

Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization

et al. 2021

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In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents a computational model formulation with associated market intelligence and game theory tools to support a decision-making process in a competitive environment. The idea behind using a market intelligence tool is to apply a stochastic optimization model with an associated conditional value at risk metric defining a utility function, which calculates the weight that the agents attribute to each stochastic variable associated with the problem to be faced. Subsequently, this utility function is used to emulate the other agents’ strategies based on their previous decisions. The final step finds the Nash equilibrium solution between a player and their competitors. The methodology is applied to the monthly allocation of firm energy by hydro generators under the current Brazilian regulatory framework. The results show a change in the generators’ behavior over the years, from risk-neutral agents seeking to maximize their return with 88% of decisions based on spot price forecasts in 2015, to risk-averse agents with 100% of decisions following a factor that is directly impacted by the hydrological affluence forecasts in 2018.

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A risk-constrained Energy Reallocation Mechanism for renewable sources with a Marginal Benefit approach

Cited by 14 publications

References 9 publications

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates

Research on risk assessment method of energy system based on data mining

Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization

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