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• A novel under-voltage load shedding (UVLS) scheme is developed using dynamic voltage stability analysis. • Detailed static and dynamic load modeling are considered, and load compositions are included for a more realistic, reliable, and effective UVLS design. • A new formulation of dynamic voltage active power sensitivity (DVPS) is formulated to determine location and amount of load shedding. This manuscript recommends an under-voltage load shedding scheme based on the sensitivity of dynamic voltage curves to mitigate voltage collapse in highly stressed power systems. The developed method can be used to determine the minimum amount and proper locations of load curtailment by considering power system dynamics including dynamic load modeling. The selected Indonesian regional system has a large number of dynamic loads in form of air conditioning and water pump loads whose dynamics contribute considerably to the complexity of the power system stability. Loss of the transmission line in the system caused by the impact of the composite load after major disturbance is studied. The proposed scheme involves an iterative procedure based on the sensitivity analysis of the dynamic voltage curve to solve the problem of load shedding. Furthermore, this work developed a new index named dynamic voltage-active power sensitivity (DVPS). The DVPS is calculated in all load buses to give information about the bus that has a predominant influence on improving system voltage stability through load shedding. Dynamic simulations are carried out with an Indonesian power system, the South Sulawesi interconnection power system with the proposed load shedding scheme. The results of this work show a noteworthy improvement in the magnitude of the voltage level as well as the voltage stability margin. [ABSTRACT FROM AUTHOR]

• • Compressed air storage alone can firm up wind and solar variability in California. • • Increased penetration of renewable energy can decrease CAES storage requirements. • • Type of renewable energy strongly affects the required CAES capacity. • • Bulk CAES deployment has limited applications due to real-world constraints. Bulk energy storage can play an important role in the decarbonization of renewable-dominant electricity system. It can offer a solution to the grid balancing problem caused by the variability in the output of renewable power generation. This paper explores the requirement for compressed air energy storage (CAES) capacity as the penetration of renewable energy increases to compensate for the variability of wind and solar. A case study for California using parsimonious macro energy model with real-world historical demand and hourly weather data has been utilized to do this analysis. In the least-cost model, with no excess wind and solar power generation, required CAES capacity is 3.71TWh in 100% decarbonized scenario. If a strict rule of net-zero curtailment was in place, the storage capacity required would be 3.2% higher (3.83TWh) with 9.8% increased cost of electricity. However, the required CAES capacity decreases with the excess solar and wind power generation. In case of 2 times increase in the wind and solar potential the required CAES capacity with strict zero curtailment would be 19.2% less (3.10TWh) and the corresponding cost would decrease up to 29.7%. The study also revealed that the type of renewable energy mix (wind, solar or both) has strong effect on the required energy storage capacity for deep decarbonization. The presented results demonstrate that excess wind and solar power generation can be used to significantly reduce the required storage needs for a fully renewable power system at reduced cost. In California, building of a large battery energy storage (up to 3 TWh) is limited by societal, geographical, and economic constraints. This study suggests that in addition to the battery energy storage California may need to look for other dispatchable power sources (or the equivalent in load flexibility) for 100% wind and solar based fully decarbonized power system. [Display omitted] [ABSTRACT FROM AUTHOR]

• An algorithm for power flow analysis considering high renewable penetration. • Regionalization of distribution system into conventional & renewable dominant regions. • Load flow investigations for both type of regions. • Minimization of power losses and less computational burden are major benefits. To harmonize the operation of renewable and conventional generation, an approach of regionalized microgrid is proposed. Regionalization has been adopted for islanded microgrid in which the distribution system has been sectionalized into conventional generation-based regions (CGRs) operating with droop control and renewable generation-based regions (RGRs) operating with constant frequency de-coupled PQ control method. Back-to-back converters have been installed between the regions for bi-directional power exchange. In this paper, a novel load flow algorithm has been proposed for a regionalized microgrid in an islanded mode that aims to solve the power flow problem for both types of the regions. The algorithm considers the uncertainty of renewable distributed generators (RDGs) and loads. The approach focuses on the frequency regulation in both types of regions. Case studies have been carried out for IEEE 15, 33 and 69-bus distribution systems by converting them into regionalized microgrids (RMG). Results have demonstrated the strength of proposed load flow approach with a fast convergence speed. [Display omitted] [ABSTRACT FROM AUTHOR]

• Study the vision of engaging networked data centers into distributed energy markets. • Propose a decision-making model to optimize the bidding decisions and energy resource operations of networked data centers in multiple geo-distributed local energy markets. • Conduct comprehensive numerical simulations to validate the benefit the cloud service provider can obtain from local energy markets. Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs' cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]