In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power flow problem in power systems. The main aim of this study is to optimize the objective functions, which are total fuel cost of generator, active power losses in transmission lines, total emission issued by fossil-fueled thermal units, voltage deviation at PQ bus, and voltage stability index. The proposed HGS approach is optimal and easy, avoids stagnation in local optima, and can solve multi-constrained objectives. Various single-and multi-objective (conflicting) functions were proposed simultaneously to solve OPF problems. The proposed algorithm (HGS) was developed to solve the multi-objective function, called the multi-objective hunger game search (MOHGS), by incorporating the proposed optimization (HGS) with Pareto optimization. The fuzzy membership theory is the function responsible to extract the best compromise solution from non-dominated solutions. The crowding distance is the strategies carried out to determine and ordering the Pareto non-dominated set. Two standard tests (IEEE 30 bus and IEEE 57 bus systems) are the power systems that were applied to investigate the performance of the proposed approaches (HGS and MOHGS) for solving single and multiple objective functions with 25 studied cases using MATLAB software. The numerical results obtained by the proposed approaches (HGS and MOHGS) were compared to other optimization algorithms in the literature. The numerical results confirmed the efficiency and superiority of the proposed approaches by achieving an optimal solution and giving the faster convergence characteristics in single objective functions and extracting the best compromise solution and well-distributed Pareto front solutions in multi-objective functions.

Optimal power flow (OPF) represents one of the most important issues in the electrical power system for energy management, planning, and operation via finding optimal control variables with satisfying the equality and inequality constraints. Several optimization methods have been proposed to solve OPF problems, but there is still a need to achieve optimum performance. A Slime Mould Algorithm (SMA) is one of the new stochastic optimization methods inspired by the behaviour of the oscillation mode of slime mould in nature. The proposed algorithm is characterized as easy, simple, efficient, avoiding stagnation in the local optima and moving toward the optimal solution. Different frameworks have been applied to achieve single and conflicting multi-objective functions simultaneously (Bi, Tri, Quad, and Quinta objective functions) for solving OPF problems. These objective functions are total fuel cost of generation units, real power loss on transmission lines, total emission issued by fossil-fuelled thermal units, voltage deviation at load bus, and voltage stability index of the whole system. The proposed algorithm SMA has been developed by incorporating it with Pareto concept optimization to generate a new approach, named the Multi-Objective Slime Mould Algorithm (MOSMS), to solve multi-objective optimal power flow (MOOPF) problems. Fuzzy set theory and crowding distance are the proposed strategies to obtain the best compromise solution and rank and reduce a set of non-dominated solutions, respectively. To investigate the performance of the proposed algorithm, two standard IEEE test systems (IEEE 30 bus IEEE 57 bus systems) and a practical system (Iraqi Super Grid High Voltage 400 kV) were tested with 29 case studies based on MATLAB software. The optimal results obtained by the proposed approach (SMA) were compared with other algorithms mentioned in the literature. These results confirm the ability of SMA to provide better solutions to achieve the optimal control variables.

As climate changes intensify the frequency of severe outages, the resilience of electricity supply systems becomes a major concern. In order to simultaneously combat the climate problems and ensure electricity supply in isolated areas, renewable energy sources (RES) have been widely implemented in recent years. However, without the use of energy storage, they show low reliability due to their intermittent output. Therefore, this article proposes a methodology to achieve the optimal sizing of an energy storage system (ESS) to ensure predefined periods of safe operation for an ensemble consisting of multiple loads, renewable energy sources and controllable generators, located in a remote microgrid. In this regard, a mixed integer linear programming (MILP) model has been proposed to reduce the outages impact of critical loads by calculating the optimal ESS capacity and defining the proper resources management within the off-grid microgrid, while ensuring a cost-effective operation of its components.

Microgrids are about to change the architecture and the operation principles of the future power systems towards smartness and resiliency. Power electronics technologies are key enablers for novel solutions. In this paper we analyze the benefits of a “microgrid by design” architecture (MDA), using a solid-state transformer (SST) as a low-voltage grid-former and inverter-based generation only. In this context, the microgrid stability is maintained with the help of “electrostatic energy inertia” that can be provided by the capacitor connected to the DC busbar behind the SST inverter topology. This happens in a natural way, alike the mechanical inertia in power systems with synchronous machines, however without depending on frequency and without the need of a rotational inertia. This type of microgrid always operates (both fully connected to the main grid or in islanding mode) with all the necessary mechanisms needed to maintain the microgrid stable—no matter of the perturbations in the upstream of the point of common coupling (PCC). In the case of microgrids with inverter-based generation only (including the energy storage systems), there is no mechanical inertia and different stability mechanisms need to be applied compared to the stability principle of the classical power systems. Our proposed mechanism differentiates from the recently proposed stability assessments of microgrids based on virtual synchronous generators from the control theory perspective. This paper is a continuation of our previous work where the MDA was first introduced. The use-cases and scenarios are based on realistic and yet reasonable complexities, by coupling the disturbance magnitude with the voltage stability limit in power grids. The paper finds meaningful disturbances to test the electrostatic energy inertia at the boundaries of grid stability, as guidance to understand the range of voltage variation for extreme conditions. The results show that in microgrids with inverter-based generation only and passive loads (RLC type) the operation is no longer frequency dependent. The energy of the DC busbar capacitor as electrostatic energy inertia of the MDA has a role similar to that of the rotational machines in classical grids in terms of maintaining dynamic stability, however impacting two different types of stability.

The concept of high-voltage DC transmission using a multiterminal configuration is presently a central topic of research and investment due to rekindled interest in renewable energy resource integration. Moreover, great attention is given to fault analysis, which leads to the necessity of developing proper tools that enable proficient dynamic simulations. This paper leverages models and control system design techniques and demonstrates their appropriateness for scenarios in which faults are applied. Furthermore, this paper relies on full-bridge submodule topologies in order to underline the increase in resilience that such a configuration brings to the multiterminal DC network, after an unexpected disturbance. Therefore, strong focus is given to fault response, considering that converters use a full-bridge topology and that overhead power lines connect the terminals.