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For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software. [ABSTRACT FROM AUTHOR]

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling. [ABSTRACT FROM AUTHOR]

In this paper, we use a nonlinear model of a grid-connected Archimedes wave swing (AWS) to explore the actual generation potential of this wave energy conversion system. Our model has never been used before for a grid-connected AWS. The control system employs six proportional-integral (PI) controllers to maximize the energy harvest from waves, to minimize the generator power losses, and to maintain both the grid and DC link voltages at their reference values. The PI controller parameters were selected using a hybrid augmented grey wolf optimizer and cuckoo search (AGWO-CS) algorithm. The results obtained from MATLAB Simulink for different types of grid fault, with successful and unsuccessful reclosing of the circuit breakers, were compared with results from the particle swarm optimization (PSO) and COOT algorithms. To verify the accuracy of our control system, a grid-connected AWS was tested experimentally using a real-time RT-LAB simulator combined with OP4510. The results validated the efficiency and superiority of the control system using AGWO-CS and the results from simulation and experiment were similar. [ABSTRACT FROM AUTHOR]

• Developing a new stochastic-interval model for energy management in communities. • The developed model considers controllable demand, vehicle-to-grid and energy storage. • The developed model allows to adopt pessimistic or optimistic strategies. • Obtained results reveal the benefits on gathering prosumers on communities. • Community operation reduces the energy that must be acquired from the grid. With the advent of smart cities, residential consumers have evolved towards the concept of prosumers. This paradigm calls for new businesses like energy communities. Energy management of such frameworks is essential to maximize the collective welfare. This paper addresses this issue by developing an energy management framework that accounts for flexible demand, storage devices and electric vehicles. Its two-level structure allows to address the energy exchanging among prosumers in the 1st stage, while the 2nd stage focuses on optimally schedule the different collective assets. A novel stochastic-interval model is proposed to handle with uncertainties from demand, renewable generation, vehicles' behaviour and energy pricing. A case study is performed on a six-prosumer community. Results serve to validate the new tool and analyse the importance of smart devices. In addition, the new proposal allows to adopt different operating strategies. Actually, the total operating cost may increase by 90% if a pessimistic point of view is adopted while the importance of the vehicle-to-grid capability is marginal otherwise. Furthermore, the energy purchased could be reduced by 7% when adopting a community arrangement, supposing an improvement in the economy and environmental indicators of the network. Other relevant aspects are identified and discussed in depth. [ABSTRACT FROM AUTHOR]

Decarbonization of the energy sector calls for new energy sources and power-system operation paradigms. In this context, consumers can partake in system operation as loads and generators by leveraging own resources, such as rooftop photovoltaics (PVs). These prosumers can be gathered into energy communities (ECs) and operated in a centralized manner to exploit eventual synergies optimally. The ECs are usually connected to a distribution network and thus interact with the distribution grid operator (DSO), complicating the scheduling strategies under agent interactions. Moreover, the DSO can thus manage his own resources such as distributed generators or medium-scale storage systems. The operation becomes more complex due to multi-source uncertainties in ECs and DSO. This paper addresses this issue by developing a novel bi-level energy management framework for the robust optimal coordination of ECs and DSO against uncertainties. In this framework, the upper-level agents provide the information by solving the designed optimization problems for the EC operation, sending it to the lower-level agents to facilitate the optimal scheduling of distributed assets. In particular, a novel robust reformulation of the lower level problem is proposed, to handle with uncertainties in renewable generation, demands, and energy prices fixed by the local retailer. The resulting formulation is mixed-integer-linear programming that is efficiently manageable by common solvers. Simulation results on the standard IEEE 33-bus distribution system with six connected ECs and various distributed assets (e.g., renewable generators and storage systems) demonstrate the effectiveness and robustness of the proposed framework. The results also evidence the vital role of storage systems in maximizing the DSO's profits while reducing the ECs' operating costs. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions. [Display omitted] • A detailed model of the WECS is presented comprehensively. • Enhancement of the transient performance of grid-connected WECS is the main goal. • A new optimization algorithm is applied to select the parameters of the PI controllers and the anti-windup coefficients. • Different scenarios have been investigated under normal and abnormal operating conditions. • The results obtained validate the robustness of the proposed controllers. [ABSTRACT FROM AUTHOR]