A Hybrid Genetic Algorithm for the Interaction of Electricity Retailers with Demand Response

Alves, Maria João; Antunes, Carlos Henggeler; Carrasqueira, Pedro

doi:10.1007/978-3-319-31204-0_30

Cited by 21 publications

(39 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concerning the variation of Pm, better results were consistently obtained with Pm=0.05, which is in accordance with the study in [12]. The algorithm seemed not to be very sensitive to the variation of , displaying very small variations for the optimal F values of the optimistic and pessimistic solution (for the best, worst and average values).…”

Section: Parameterssupporting

confidence: 75%

“…The input data are similar to the data used in [12] except for the comfort time slots allowed for the operation of each load and the degree of dissatisfaction.…”

Section: Problem Datamentioning

confidence: 68%

“…The bilevel models mentioned above do not include the operation cycles of the household appliances (apart from the model proposed in [12]). The operation cycles define the power requested to the grid by each appliance in each stage of operation (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Alves et al [12] proposed a hybrid approach consisting of a genetic algorithm and an exact MILP solver to deal with a bilevel problem in which the retailer establishes prices to maximize profits and consumers are able to deviate consumption of shiftable loads to minimize costs. Constraints impose specific time slots for load operation, which may decrease the retailer's profit.…”

Section: Introductionmentioning

confidence: 99%

“…This model is based on the scalar bilevel model presented in [12]. The retailer is the leader and wishes to determine tariffs that maximize his profit.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Alves

Antunes

2018

Computers & Operations Research

Self Cite

View full text Add to dashboard Cite

Presently, residential electricity consumers are, in general, charged at flat or dual time-of-use tariffs along the day, which are defined by the retailer for long periods (e.g., one year). These pricing schemes do not convey price signals reflecting generation costs and grid conditions. Hence, consumers lack the incentives to engage in different consumption patterns using the flexibility they generally have in the operation of some end-use loads. Dynamic tariffs, i.e. energy prices varying possibly with significant magnitude in short periods of time, are expected to become an applicable pricing scheme in smart grids. In this setting, home energy management systems can play an important role to help end-users optimizing the usage of appliances to minimize energy costs without compromising comfort. This can be advantageous also from the perspective of grid management.A semivectorial bilevel programming approach is developed to model the interaction between electricity retailers and consumers in order to optimize electricity time-of-use retail pricing. The aim is to support the retailer in finding good decisions for the prices. The retailer (upper level decision maker) establishes dynamic time-of-use electricity prices to maximize profits. The consumer (lower level decision maker) responds by selecting, under that price setting, a load scheduling decision leading to a nondominated solution balancing his objectives of minimizing the electricity bill (cost dimension) and minimizing the dissatisfaction in face of his preferences and requirements (comfort dimension).The lower level optimization problem is formulated as a bi-objective mixed-integer linear programming problem. A hybrid approach is proposed, which consists of a genetic algorithm for the upper level problem and an exact solver to solve surrogate scalar problems at the lower level. A case study is presented and discussed.

show abstract

Section: Parameterssupporting

confidence: 75%

“…The input data are similar to the data used in [12] except for the comfort time slots allowed for the operation of each load and the degree of dissatisfaction.…”

Section: Problem Datamentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This model is based on the scalar bilevel model presented in [12]. The retailer is the leader and wishes to determine tariffs that maximize his profit.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Alves

Antunes

2018

Computers & Operations Research

Self Cite

View full text Add to dashboard Cite

show abstract

Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming

Alves

Antunes

Soares

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

In this paper, a comparison is made between two bilevel programming models to design time-of-use tariffs in the electricity retail market. The upper-level objective function consists of the maximization of the retailer's profit and the lower-level problem relates to the minimization of the consumer's cost. In the first model, the periods in which prices apply are pre-defined and the aim is to determine the price values. In the second model, which is developed for the first time in this paper, both the periods and prices are decision variables, thus leading to a very large search space for the upper-level problem due to the number of combinations periods-prices. For the model with variable periods, a hybrid approach combining a genetic algorithm for the upper-level search with a mixed-integer linear programming solver to obtain optimal solutions to the lower-level problem is herein developed. Computational results comparing the two models are presented.

show abstract

Comparison of Thermal Load Models for MILP-Based Demand Response Planning

Magalhães

Antunes

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

A Hybrid Genetic Algorithm for the Interaction of Electricity Retailers with Demand Response

Cited by 21 publications

References 6 publications

A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming

Comparison of Thermal Load Models for MILP-Based Demand Response Planning

Contact Info

Product

Resources

About