The Nomadic People Optimizer applied to the economic dispatch problem with prohibited operating zones Lucas Santiago Nepomuceno Department of Energy Federal University of Juiz de Fora Juiz de Fora, Minas Gerais, Brazil lucas.nepomuceno@engenharia.ufjf.br Gabriel Schreider da Silva Department of Energy Federal University of Juiz de Fora Juiz de Fora, Minas Gerais, Brazil gabriel.schreider2016@engenharia.ufjf.br Edimar Jose de Oliveira Department of Energy Federal University of Juiz de Fora Juiz de Fora, Minas Gerais, Brazil edimar.oliveira@ufjf.edu.br Arthur Neves de Paula Department of Energy Federal University of Juiz de Fora Juiz de Fora, Minas Gerais, Brazil arthur.paula@engenharia.ufjf.br Edmarcio Antonio Belati CECS Federal University of ABC Santo Andr´ e, S˜ ao Paulo, Brazil edmarcio.belati@ufabc.edu.br Abstract—This work proposes the application of the Nomadic People Optimizer (NPO) to solve the economic dispatch problem considering Prohibitive Operating Zones (POZ). The NPO is a swarm-based metaheuristic recently introduced in the literature and still under-explored. In addition, the POZ increase the difficulties to find the optimal solution of the economic dispatch problem. The performance of the proposed methodology is com- pared with others metaheuristics present in the literature. Also, a sensibility analysis was performed. The NPO performed better than Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) metaheuristics in solving the problem. Keywords: Economic dispatch. Nomadic People Optimizer. Prohibited operating zones. Metaheuristics. Bio-inspired opti- mization. I. I NTRODUCTION The fossil fuels, widely used in thermal power plants (TPP), have a high-cost value when compared to other primary sources used in other forms of electricity generation, such as solar and wind.An aggravating factor for this situation is the currently hydric crisis that Brazil is facing, in which the water reservoir of the hydraulic power plants - main source of electricity generation in the country - reached the lowest level in the last 91 years. In this scenario, the use of TPPs to generate electricity is intensified to keep the load supply of the National Interconnected System, which causes a significant increase of the generation cost of the system. The country broke the historical record of electricity generation through TPP, producing 18787.78 MW in average on May 31, 2021, a level never before reached [1]. Therefore, it is clear the importance of optimal dispatch of TPPs aiming to minimizing the generation cost. The economic dispatch (ED) problem is characterized by the TPPs dispatch aiming to minimize the system’s generation cost, i.e., to determine the active power outputs of TPPs so that the generation cost is as low as possible [2]. Therefore, the ED problem consists in an optimization problem where the objective function (OF) seeks to minimize the generation cost. Also, there is some constrains that must be respected [3]. These constrains are related to the maximum and min- imum generation limits, due to the operational limits of the generators, and the system load demand plus the losses [4]. In most cases involving the ED problem, an approximate quadratic function relates the generation costs to the active power dispatch, constituting the OF [5, 6]. However, this representation of the ED problem is not very realistic, as it disregards the practical constraints of the TPP generator machines. To achieve a more realistic representation of the ED problem, prohibitive operating zones (POZs) can be considered, which are related to auxiliary services of TPPs, among others, such as boilers and feed pumps, which define operating regions unstable that should be avoided [7]. With the inclusion of the POZs, more inequalities are added to the set of constraints of the ED problem and the cost curve of a TPP is now interpreted as a curve determined by these inequalities, being divided into several isolated sub-regions, which form multiple spaces of decision, this makes ED a non- convex and discontinuous problem, causing a large increase in its complexity [7–9]. The metaheuristics are stochastic optimization methods ca- pable of solving several problems in a generic way [10]. In the field of power systems, optimization via the metaheuristic technique is widely used in problems such as transmission and distribution expansion planning, economic dispatch, power flow optimization, load forecasting, among others [11]. The metaheuristics are generally simple and easy to implement, in addition to being flexible to modify their structure and param-