3.2.3. Heuristic and Metaheuristic Approaches

Heuristic and metaheuristic approaches are used in many disciplines, such as in telecommunications and transportation systems. Recent studies have developed EM approaches for MG systems. For instance, the authors of [94] introduced a heuristic method for the optimal operation and EM of DC MG systems. The studied problem was formulated in the form of a single-objective optimization problem by focusing only on cost minimization. The authors of [95] proposed a metaheuristic based system by integrating the Harmony search algorithm and the enhanced differential evolution. To ensure that the power consumption did not exceed a fixed threshold value during peak periods, multiple knapsacks were used, and the proposed system outperformed the existing metaheuristic techniques in terms of cost and peak-to-average ratio. The authors of [96] proposed an economical model for energy storage system together with a real coded-genetic algorithm model for MG systems operating in a grid-connected mode. The developed algorithm maximized the present cost of energy storage system over its lifespan based on its capital, energy arbitrage revenue, operation cost, and maintenance cost. The authors of [97] proposed an optimal EM system for a grid-connected MG system based on the genetic algorithm, which considered the electricity price, power consumption, and uncertainty of RES generation. The work showed that particle swarm optimization method is more efficient in term of finding the best solution of the studied optimization function in comparison with genetic algorithm and combinatorial particle swarm optimization. A deterministic EM problem was solved by the authors of [98] via the multi-period gravitational search algorithm. The authors of [99] used a multi-objective particle swarm optimization algorithm to solve the EM system problem, which was considered as a multi-objective problem. However, the authors of [13,100] solved the EM system problem as a single-objective problem using particle swarm optimization-based algorithms. A metaheuristic approach for MG configuration in green data centers was presented by the authors of [101]. An optimization model was presented that considered the electricity costs and greenhouse gas emissions associated with all components of the MG systems, as well as their interactions. The model was applied to a real scenario of a data center with a given load demand in a specified environment. The authors calculated the degradation costs and the operational cost based on a system lifetime of 20 years. The developed model ensured good-quality MG configurations for different tradeoffs of cost and sustainability. Another work, presented by the authors of [102], combined an intelligent expert system fuzzy logic and a metaheuristic algorithm Grey-Wolf Optimizer. The proposed approaches solved the economic and environmental optimization problems of the MG systems by considering the uncertainties of RES and fluctuation in the power demand. In addition, a monitoring technique was developed with the fuzzy system to evaluate the input parameters to control the battery charge/discharge cycle, taking into account the economic aspect of the Grey-Wolf Optimizer optimization problem. The battery storage system operated by tracking the local generation costs of the installed MG and the total costs of the battery storage, which increased the possibility of charging the storage system at low costs during off-peak times. A metaheuristic home energy management system was studied by the authors of [103]. The authors evaluated the performance of the home energy management system using three metaheuristic optimization techniques: Bacterial foraging optimization, the Harmony search algorithm, and Enhanced deferential evolution. The objectives were to minimize the energy consumption, electricity cost, and reduction in peak-to-average ratio while maximizing user comfort. The obtained results showed that a tradeoff between user comfort and cost exists for the control constraints. In terms of cost, the results showed that the Harmony search algorithm performs better among other techniques. Another new interesting work, presented by the authors of [104], used a metaheuristic-based vector-decoupled algorithm to balance the control and operation of a hybrid MG system in the presence of stochastic renewable energy sources and the electric vehicle charging structure. The proposed control method ensured the stability of both frequency and voltage levels during the high-pulsed demand conditions and severe conditions of islanding operation mode together with the variability of RESs production. The presented results exposed the effectiveness and robustness of the proposed method to manage the real and reactive power exchange between the installed DC and AC buses of the MG within acceptable voltage and frequency variability.

Generally, heuristic optimization approaches use exploratory methods, in a reasonable time, to solve the optimization problems. However, they are unable to assure optimality of the obtained results [105]. The metaheuristic approaches are efficient and popular methods that are used for control and EM in the MG system. Several works in the literature that have analyzed the performance of these approaches. In some works, the metaheuristic control has been coupled with other control approaches in order to benefit from the performance of both approaches [106,107].
