scholarly journals Optimisation for Coalitions Formation Considering the Fairness in Flexibility Market Participation

2021 ◽  
Vol 239 ◽  
pp. 00016
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Fernando Lezama ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Differential Evolution ◽  
Electricity Markets ◽  
Coalition Formation ◽  
Electricity Market ◽  
Optimization Model ◽  
End Users ◽  
Model Based ◽  
Coalition Structures ◽  
Proposed Model ◽  
Local Areas

This paper proposes a coalitional game-theoretical model for consumers’ flexibility coalition formation, supported by an optimization model based on differential evolution. Traditionally, the participation in conventional electricity markets used to be limited to large producers and consumers. The final end-users contract their energy supply with retailers, since due to the smaller quantity available for trading, they cannot participate in electricity market transactions. Nowadays, the growing concept of local electricity market brings many advantages to the end-users. The flexibility negotiation considering local areas is an important procedure for network operators and it is incorporating a local electricity market opportunity. A coalition formation model to facilitate small players participation in the flexibility market proposed by the network operator is addressed in this work. The inclusion of Shapley value in the proposed model enables finding the best coalition structures considering the fairness of the coalitions in addition to the potential income achieved by the consumers when selling their flexibility. An optimization model based on differential evolution is also proposed as the way to find the optimal coalition structures based on the multi-criteria specifications.

scholarly journals Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1610 ◽  
Author(s):  
Danijel Topić ◽  
Marinko Barukčić ◽  
Dražen Mandžukić ◽  
Cecilia Mezei
Keyword(s):  
Power Plant ◽  
Differential Evolution ◽  
Optimization Model ◽  
Power Plants ◽  
Differential Evolution Algorithm ◽  
Transportation Costs ◽  
Proposed Model ◽  
Transportation Distance ◽  
Evolution Algorithm ◽  
Plant Feedstock

In this paper, an optimization model for biogas power plant feedstock mixture with respect to feedstock and transportation costs using a differential evolution algorithm (DEA) is presented. A mathematical model and an optimization problem are presented. The proposed model introduces an optimal mixture of different feedstock combinations in a biogas power plant and informs about the maximal transportation distance for each feedstock before being unprofitable. In the case study, the proposed model is applied to five most commonly used feedstock in biogas power plants in Croatia and Hungary. The research is performed for a situation when the biogas power plant is not owned by the farm owner but by a third party. An optimization procedure is performed for each scenario with a cost of methane production that does not exceed 0.75 EUR/m3 in 1 MWe biogas power plant. The results show the needed yearly amounts and the maximum transportation distance of each feedstock.

scholarly journals A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Radhakrishnan Angamuthu Chinnathambi ◽  
Anupam Mukherjee ◽  
Mitch Campion ◽  
Hossein Salehfar ◽  
Timothy Hansen ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Moving Average ◽  
Percentage Error ◽  
Spot Prices ◽  
Multi Stage ◽  
Proposed Model ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Stage 1

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

scholarly journals A Novel E-Commerce Negotiation Optimization Model Based on Improved Genetic Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 566-570
Author(s):  
Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Electronic Commerce ◽  
Value Creation ◽  
Optimization Model ◽  
Improved Genetic Algorithm ◽  
Model Based ◽  
Proposed Model ◽  
Business Market ◽  
Major Player ◽  
The Relationship

Electronic commerce has rapidly become a major player in the business market .This paper proposes a new electronic commerce negotiation optimization model based on improved genetic algorithm which depends on not only price, but also other factors of commodity. The proposed model illustrates the relationship between the business components required to support the e-commerce processes with the value creation factor and the controlling complexity. The experiment results show that the proposed algorithm can gain the optimal negotiation result more efficiently than other three kinds of negotiation algorithms in competitive bilateral multi-issue negotiation.

scholarly journals Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 293
Author(s):  
Sergio Cantillo-Luna ◽  
Ricardo Moreno-Chuquen ◽  
Harold R. Chamorro ◽  
Jose Miguel Riquelme-Dominguez ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Machine Learning ◽  
Electricity Markets ◽  
Electricity Market ◽  
High Performance ◽  
Support Vector ◽  
Average Prediction Error ◽  
Market Signals ◽  
Proposed Model ◽  
Power Load ◽  
Locational Marginal Price

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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