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

Abstract

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.

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

“…With this purpose, the CE-CMAES algorithm was applied to solve the problem. To prove its effectiveness, the results of CE-CMAES were compared with 11 algorithms participating in the 2020 competition on "Evolutionary Computation in the Energy Domain: Smart Grid Applications" [36,38]. The MATLAB™ codes of the participating algorithms are available at http://www.gecad.isep.ipp.pt/ERM-competitions/2020-2 (accessed on 18 June 2022).…”

Brute Exhaustive Optimization of Intelligent Small Weighted Voting Ensembles in 1EXP(-)Z+ Initial-Term based Arithmetic Sequence’s Multi Precision Search Spaces

“…Brute or exhaustive search algorithm is a set of instruction used to find optimal solution by examining all possible solution combinations. This search process is not that new at all, it has been applied in several optimization problems to search for the most deemed optimal solution [12,23,25].…”

“…Also, in [26] static and dynamic predictor weighting strategies were implemented and tested to improve the analog ensemble performance for wind power forecasting at on and offshore wind farms by using a brute force search procedure with error minimization over all possible predictor combinations. Usually, the general basic algorithm that follows an exhaustive or brute force search require two main stages: namely, Listing all the possible candidate solutions in a systematic way, and checking for the optimal solution and reporting it [12]. While the main disadvantage of brute exhaustive technique being its requirement for massive computational resources in order to find solutions in very large search spaces and which may sometimes makes it slow and infeasible [27], a drawback which can be addressed by using the search space reduction and algorithm parallelization strategies such as using parallel CPU-GPU computing structure.…”

“…However, although both the stochastic population-based evolutionary and greedybased search heuristic procedures are often more efficient than brute exhaustive search, they may sometimes not guarantee to achieve of global optimum [6,7], whereas greedy and its variant implementation, such as the greedy randomized adaptive search which have been used by (8) may face a hill climbing problem, the evolutionary extremums may be caused by its population-based stochastic search heuristic implementation which may probabilistically select at that one time from a very unfit initialized genes chromosomes of the creature being optimized [9], among other things. As such, as observed in [10], the surprising outstanding successes of the systematic brute force-based exhaustive search counterpart in producing optimal WVE models configuration sets with predictive performances similar to those created by evolutionary-based optimization procedures in conjunction with its theoretical guarantee for finding an optimal solution through a search across systematic search spaces [11], it may become imperative to implement the brute exhaustive search procedures, as given the required high computational effort is available, it guarantees exhaustion of all candidate solutions combinations [11,12], for optimality search problems, such as this of finding the appropriate weights for the most accurate WVE, at a reasonable efficiency tradeoff when the deemed global optima solution estimations has been defined as a key requirement, that is, must occur.…”

Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems

“…The authors of [28] solved the reactive power optimization problem by using an adaptive differential evolution method. Several heuristic algorithms were utilized for parallel computing in solving bidding problems in local energy markets in [29]. A parallel particle swarm optimization was used in [30] to maximize the profits of the industrial customers that provide operational services to the power grid.…”