Operation of The Hybrid Energy Resources with Storage System Participation

This paper focused on the optimal operation of the hybrid energy resources in the off-grid state considering energy storage participation. The hybrid energy resources consist of wind turbine (WT), photovoltaic (PV), diesel generator (DG), and energy storage system for supplying energy to DC and AC load demand with maximum reliability. The operation of the proposed energy system based on energy control and energy optimization is modeled. The energy optimization and energy control are implemented by heuristic and nonlinear quadratic programming approaches via optimal power flow on the resources side. The energy control is done based on the weight sum method in different operation states of the system. Also, the impact of the energy storage system on the hybrid energy resources is considered as backup resources. The energy control modeling is implemented via mathematical simulation and numerical analysis in the two operation states in the summer and winter seasons for verifying the proposed approaches. Finally, the results of the energy control show optimal states of the energy system in supplying demand with considering the energy storage system

Operation of The Hybrid Energy Resources with Storage System Participation

This paper focused on the optimal operation of the hybrid energy resources in the off-grid state considering energy storage participation. The hybrid energy resources consist of wind turbine (WT), photovoltaic (PV), diesel generator (DG), and energy storage system for supplying energy to DC and AC load demand with maximum reliability. The operation of the proposed energy system based on energy control and energy optimization is modeled. The energy optimization and energy control are implemented by heuristic and nonlinear quadratic programming approaches via optimal power flow on the resources side. The energy control is done based on the weight sum method in different operation states of the system. Also, the impact of the energy storage system on the hybrid energy resources is considered as backup resources. The energy control modeling is implemented via mathematical simulation and numerical analysis in the two operation states in the summer and winter seasons for verifying the proposed approaches. Finally, the results of the energy control show optimal states of the energy system in supplying demand with considering the energy storage system

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights

Indicator Based Ant Colony Optimization for Multi-objective Knapsack Problem

AbstractThe use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many algorithms have been proposed in the literature to solve different MOP. This paper presents an indicator-based ant colony optimization algorithm called IBACO for the multi-objective knapsack problem (MOKP). The IBACO algorithm proposes a new idea that uses binary quality indicators to guide the search of artificial ants. These indicators were initially used by Zitzler and Künzli in the selection process of their evolutionary algorithm IBEA. In this paper, we use the indicator optimization principle to reinforce the best solutions by rewarding pheromone trails. We carry out a set of experiments on MOKP benchmark instances by applying the two binary indicators: epsilon indicator and hypervolume indicator. The comparison of the proposed algorithm with IBEA, ACO and other state-of-the-art evolutionary algorithms shows that IBACO is significantly better on most instances

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such models is a problem with considerable theoretical interest that still poses many challenges. On the one hand, this is a well-known NP-complete problem, which is practically hardened by the huge search space of possible solutions. On the other hand, the phenomenon of I-equivalence, i.e., different graphical structures underpinning the same set of statistical dependencies, may lead to multimodal fitness landscapes further hindering maximum likelihood approaches to solve the task. Despite all these difficulties, greedy search methods based on a likelihood score coupled with a regularization term to account for model complexity, have been shown to be surprisingly effective in practice. In this paper, we consider the formulation of the task of learning the structure of Bayesian Networks as an optimization problem based on a likelihood score. Nevertheless, our approach do not adjust this score by means of any of the complexity terms proposed in the literature; instead, it accounts directly for the complexity of the discovered solutions by exploiting a multi-objective optimization procedure. To this extent, we adopt NSGA-II and define the first objective function to be the likelihood of a solution and the second to be the number of selected arcs. We thoroughly analyze the behavior of our method on a wide set of simulated data, and we discuss the performance considering the goodness of the inferred solutions both in terms of their objective functions and with respect to the retrieved structure. Our results show that NSGA-II can converge to solutions characterized by better likelihood and less arcs than classic approaches, although paradoxically frequently characterized by a lower similarity to the target network

An integrated optimization approach to locate the D-STATCOM in power distribution system to reduce the power loss and total cost

The optimization problem with a single objective can obtain a single solution, called an optimal solution. It maximizes or minimizes the performance of a particular objective function to a given constraint. But, in the case of the multi-objective optimization, different objectives can be simultaneously optimized. Thus, this paper recommends a multi-objective optimization methodology for simultaneously perform the two objective functions such as resizing and optimal placement of Distributed Static Compensator (DSTATCOM) for reducing the power loss, total cost and enhancing the voltage profile. For these purposes, an integrated approach of two optimization algorithm called Multi-objective Ant Colony Optimization (MACO) and Bacterial Foraging Optimization Algorithm (BFOA) are used. The prime intention of this work is to bring down the power loss, total cost and enhance the voltage profile by placing the DSTATCOM device in an optimal location. Here, IEEE-30 and IEEE-69 bus systems are considered to appraise the recital of the recommended approach. Moreover, the effectiveness of the MACO-BFOA approach is evaluated and compared with other multi-objective algorithms. From this analysis, it is observed that when compared to these techniques, the proposed system provides the minimized power loss and total cost

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the e- dominance relation was used to update the archive population solutions. e-dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems

Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling

open access articleMinimising the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimisation algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii