Energy Portfolio Optimization for Electric Utilities: Case Study for Germany

2010 ◽  
pp. 221-246 ◽  
Author(s):  
Steffen Rebennack ◽  
Josef Kallrath ◽  
Panos M. Pardalos
Keyword(s):  
Portfolio Optimization ◽  
Electric Utilities ◽  
Energy Portfolio Optimization

scholarly journals Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

scholarly journals Portfolio optimization of power plants by using renewable energy in Iran

2020 ◽  
Author(s):  
Seyedeh Asra Ahmadi ◽  
Seyed Mojtaba Mirlohi ◽  
Mohammad Hossein Ahmadi ◽  
Majid Ameri
Keyword(s):  
Renewable Energy ◽  
Portfolio Optimization ◽  
Energy Supply ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Modern Society ◽  
Sustainable Growth ◽  
Electricity Sector ◽  
Capital Costs

Abstract Lack of investment in the electricity sector has created a huge bottleneck in the continuous flow of energy in the market, and this will create many problems for the sustainable growth and development of modern society. The main reason for this lack of investment is the investment risk in the electricity sector. One way to reduce portfolio risk is to diversify it. This study applies the concept of portfolio optimization to demonstrate the potential for greater use of renewable energy, which reduces the risk of investing in the electricity sector. Besides, it shows that investing in renewable energies can offset the risk associated with the total input costs. These costs stem from the volatility of associated prices, including fossil fuel, capital costs, maintenance, operation and environmental costs. This case study shows that Iran can theoretically supply ~33% of its electricity demand from renewable energy sources compared to its current 15% share. This case study confirms this finding and predicts that Iran, while reducing the risk of investing in electricity supply, can achieve a renewable energy supply of ~9% with an average increase in supply costs. Sensitivity analysis further shows that with a 10% change in input cost factors, the percentage of renewable energy supply is only partially affected, but basket costs change according to the scenario of 5–32%. Finally, suggestions are made that minimize risk rather than cost, which will bring about an increase in renewable energy supply.

Energy Strategy Planning for Electric Utilities Part II, Smarte Methodology Case Study

1982 ◽  
Vol PAS-101 (2) ◽  
pp. 347-355 ◽  
Author(s):  
Dimitrios Aperjis ◽  
David White ◽  
Fred Schweppe ◽  
Matthew Mettler ◽  
Hyde. Merrill
Keyword(s):  
Electric Utilities ◽  
Energy Strategy ◽  
Strategy Planning

Emergency Prediction In Electric Utilities: A Case Study From South Brazil

2016 ◽  
Author(s):  
Iochane Guimaraes ◽  
Vinicius Jacques Garcia ◽  
Daniel Pinheiro Bernardon ◽  
Julio Fonini
Keyword(s):  
Electric Utilities ◽  
South Brazil

scholarly journals Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2249 ◽  
Author(s):  
Emanuel Canelas ◽  
Tânia Pinto-Varela ◽  
Bartosz Sawik
Keyword(s):  
Portfolio Optimization ◽  
Electricity Market ◽  
Value At Risk ◽  
Programming Model ◽  
Spot Market ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Expected Cost ◽  
Seasonal Behavior

Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and the conditional value-at-risk (CVaR) minimization are used as objective function. Three case studies illustrate the model applicability through the characterization of how the portfolio evolves with different demand profiles and how to take advantage from seasonality characteristic in the spot market. A scenario analysis is explored to reflect the uncertainty on the price of the spot market. The expected cost and CVaR are optimized for each case study and the portfolio analysis for each risk posture is characterized. The results illustrate the advantage to reduce costs and risk if the prices seasonality is considered, triggering to an adaptive seasonal behavior, which support the decision-maker decision towards its goals.

scholarly journals Energy Portfolio Optimization of Data Centers

2017 ◽  
Vol 8 (4) ◽  
pp. 1898-1910 ◽  
Author(s):  
Mahdi Ghamkhari ◽  
Adam Wierman ◽  
Hamed Mohsenian-Rad
Keyword(s):  
Portfolio Optimization ◽  
Data Centers ◽  
Energy Portfolio Optimization

Selecting and Optimizing a Regulation Compliant Robust Vehicle Portfolio Mix: An Approach and a Case Study

2012 ◽  
Author(s):  
Anil Kumar Maddulapalli ◽  
Parameshwaran S. Iyer ◽  
N. R. Srinivasa Raghavan
Keyword(s):  
Portfolio Optimization ◽  
Consumer Preferences ◽  
Traditional Approach ◽  
Mixed Integer ◽  
Preference Heterogeneity ◽  
Corporate Average Fuel Economy ◽  
Fuel Price ◽  
Contribution Margin ◽  
Portfolio Decisions

Our goal is to select a robust vehicle portfolio mix and optimize its design attributes such that contribution margin is maximized while being regulation compliant under varying fuel prices. Compliance to regulation is measured in terms of the Corporate Average Fuel Economy or CAFE. We formulate this vehicle portfolio optimization problem as a mixed integer non-linear programming problem, both under static and varying fuel price scenarios. We demonstrate our approach using a case study in which an in-house market simulator is employed for incorporating consumer preferences in portfolio decisions. This market simulator uses real-time preferences from tens of thousands of shoppers and captures preference heterogeneity using different Logit coefficients for each shopper and hence is computationally expensive. Also, it does not explicitly model the influence of fuel price in predicting demand. To overcome these issues and to facilitate portfolio optimization we use meta-models of the market simulator. Our results show that while remaining regulation compliant it is also possible to achieve significant improvement in the portfolio’s contribution margin. In some scenarios, the improvements in contribution margin are more than 40% when compared to the traditional approach of using expert judgment to decide the portfolio mix.

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