scholarly journals Algorithms for Bidding Strategies in Local Energy Markets: Exhaustive Search through Parallel Computing and Metaheuristic Optimization

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 269
Author(s):  
Andrés Angulo ◽  
Diego Rodríguez ◽  
Wilmer Garzón ◽  
Diego F. Gómez ◽  
Ameena Al Sumaiti ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Power Systems ◽  
Search Algorithm ◽  
Exhaustive Search ◽  
Metaheuristic Algorithms ◽  
Bidding Strategies ◽  
Metaheuristic Optimization ◽  
Metaheuristic Methods ◽  
Global Optima ◽  
And Performance

The integration of different energy resources from traditional power systems presents new challenges for real-time implementation and operation. In the last decade, a way has been sought to optimize the operation of small microgrids (SMGs) that have a great variety of energy sources (PV (photovoltaic) prosumers, Genset CHP (combined heat and power), etc.) with uncertainty in energy production that results in different market prices. For this reason, metaheuristic methods have been used to optimize the decision-making process for multiple players in local and external markets. Players in this network include nine agents: three consumers, three prosumers (consumers with PV capabilities), and three CHP generators. This article deploys metaheuristic algorithms with the objective of maximizing power market transactions and clearing price. Since metaheuristic optimization algorithms do not guarantee global optima, an exhaustive search is deployed to find global optima points. The exhaustive search algorithm is implemented using a parallel computing architecture to reach feasible results in a short amount of time. The global optimal result is used as an indicator to evaluate the performance of the different metaheuristic algorithms. The paper presents results, discussion, comparison, and recommendations regarding the proposed set of algorithms and performance tests.

Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms

2018 ◽  
Vol 03 (02) ◽  
pp. 1850009 ◽  
Author(s):  
Amandeep Kaur Virk ◽  
Kawaljeet Singh
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Search Algorithm ◽  
Packing Problem ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Two Dimensional ◽  
Rectangle Packing ◽  
Metaheuristic Methods ◽  
Rectangle Packing Problem

This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search techniques which can be found to be used widely in the literature to solve this problem. Good optimal solutions were obtained from all the techniques and the new metaheuristic algorithms performed better than genetic algorithm and tabu search. It was seen that cuckoo search algorithm excels in performance as compared to the other techniques.

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

2021 ◽  
pp. 136943322110262
Author(s):  
Mohammad H Makiabadi ◽  
Mahmoud R Maheri
Keyword(s):  
Standard Deviation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Minimum Mass ◽  
Multiple Frequency ◽  
Frequency Constraints ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dynamic Frequency

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

scholarly journals Opening Up Transactive Systems: Introducing TESS and Specification in a Field Deployment

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3970
Author(s):  
Marie-Louise Arlt ◽  
David P. Chassin ◽  
L. Lynne Kiesling
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Supply And Demand ◽  
System Level ◽  
Market Mechanisms ◽  
Bidding Strategies ◽  
Incentive Problems ◽  
Tariff Structures ◽  
Field Deployment ◽  
Residential Demand

Transactive energy systems (TS) use automated device bidding to access (residential) demand flexibility and coordinate supply and demand on the distribution system level through market processes. In this work, we present TESS, a modularized platform for the implementation of TS, which enables the deployment of adjusted market mechanisms, economic bidding, and the potential entry of third parties. TESS thereby opens up current integrated closed-system TS, allows for the better adaptation of TS to power systems with high shares of renewable energies, and lays the foundations for a smart grid with a variety of stakeholders. Furthermore, despite positive experiences in various pilot projects, one hurdle in introducing TS is their integration with existing tariff structures and (legal) requirements. In this paper, we therefore describe TESS as we have modified it for a field implementation within the service territory of Holy Cross Energy in Colorado. Importantly, our specification addresses challenges of implementing TS in existing electric retail systems, for instance, the design of bidding strategies when a (non-transactive) tariff system is already in place. We conclude with a general discussion of the challenges associated with “brownfield” implementation of TS, such as incentive problems of baseline approaches or long-term efficiency.

A variable neighborhood search algorithm for optimal protection coordination of power systems

2021 ◽  
Author(s):  
H. R. E. H. Bouchekara ◽  
M. S. Shahriar ◽  
M. S. Javaid ◽  
Y. A. Sha’aban ◽  
M. Zellagui ◽  
...  
Keyword(s):  
Power Systems ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Neighborhood Search Algorithm ◽  
Optimal Protection

scholarly journals Systematic Literature Review on Metaheuristic Optimization Techniques in WSNs

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Systematic Review ◽  
Wireless Sensor Networks ◽  
Exact Solution ◽  
Energy Consumption ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Wireless Sensor ◽  
Time Constraints ◽  
Metaheuristic Optimization ◽  
New Algorithms

Metaheuristic algorithms are recognized for developing new algorithms and optimizing various aspects in Wireless Sensor Networks (WSNs). Evaluating a multitude of possible modes is required, in most complicated problems, to obtain an exact solution. Metaheuristic algorithms can obtain solutions in acceptable time constraints. These algorithms play an operational role in solving such problems by optimizing the different metrics such as coverage rate and energy consumption of the networks. These metrics have valuable impact on network lifetime as well. This systematic review focuses on the published work from 2010 to 2020 in metaheuristic optimization in WSN. Furthermore, the systematic review will answer multiple questions that will be discussed in the methodology section.

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