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The broad spread of renewable energy sources (RESs) and storage systems increases modern power systems' challenges and may conflict with system operation requirements. Determination of the maximum hosting capacity (HC) is crucial for utilities to estimate the maximum capacity of RESs and storage units that a network can accommodate efficiently. Several models were developed in the literature that lacks studying the impact of reliability requirements and the size of other network components on the HC levels. This paper proposes a framework to effectively maximize the hosting of RESs and storage systems in power systems. An AC optimal power flow is used to formulate the problem and consider the networks' real operation. The objective function is formulated to expand transmission lines, allocate fault current limiters (FCLs), and ensure HC enhancement. The AC models are always complex and non-convex, and finding optimal solutions is critical. A hybrid artificial rabbits sine–cosine algorithm is developed to solve the problem, and its performance is compared to some well-known metaheuristics. The numerical studies on the Garver network and the IEE 24-bus system demonstrated that the size of different power technologies directly impacted the amount of RESs deployed. The penetration of RESs and storage systems was maximized by increasing the number of candidate circuits. The results revealed a 22 % increase in the size of RESs installed in the Garver network. Also, a 25.7 % increase in the size of RESs for the 24-bus system was obtained due to the increased candidate transmission lines. Further, an increase of 49 % was noticed in the size of storage systems built for the 24-bus system. The use of FCLs was necessary to maintain the short-circuit current below the required values. For both systems, the size of FCLs required was increased by 24.3 % and 28 %, respectively. The results also showed that the hybrid artificial rabbits sine–cosine optimizer was more efficient than other algorithms in solving the investigated problem. [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]

Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system's stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes' law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system's performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and particle swarm optimization (PSO) were compared to ensure the efficacy of the introduced optimization algorithm. The simulation results showed the effectiveness of the optimally controlled SMES system, using the AOA in smoothing the output power variations and increasing the stability of the system under various operating conditions. [ABSTRACT FROM AUTHOR]

With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer. [ABSTRACT FROM AUTHOR]

This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation coefficients to improve transient stability. The PI gains are optimized using the Golden Jackal Optimization Algorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm Optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable energy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work. • A hybrid wave and PV system is proposed. • A nonlinear model of the AWS wave energy system is presented. • A novel optimal control based on the GJOA is described. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. [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]

• An optimal bidding strategy for microgrids in clusters is developed. • A multi-stage framework is proposed for considering response fatigue. • Uncertainty risk is managed by enforcing the CVaR. • The total number of response signals can be reduced by 76 %. • The response fatigue minization may reduce the collective welfare by 18 %. The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account. [ABSTRACT FROM AUTHOR]