The large-scale adoption of electric vehicles (EVs) triggers the need to deploy more and more EV charging stations. This paper performs the techno-environmental-economic analysis of the EV charging station deployment in a residential area in Singapore. We first forecast the EV penetration rate and then analyze the charging patterns and drivers' behavior to determine the optimal deployment strategy. The proposed deployment strategy suggests suitable types, optimal quantities, and estimated costs of EV chargers. A sensitivity analysis of the EV penetration rate is further conducted, comprehensively considering technological, environmental, and economic factors. This study is expected to provide the implications of the techno-economic-environmental benefits and challenges of large-scale EV integration in residential areas. This could further facilitate policymakers in advocating the developmental goals of EVs sand motivate citizens to adopt EVs.

Off-grid solar photovoltaic (PV) systems are a vital solution to electrification in remote or rural areas where the grid connection is not feasible due to geographical constraints and high deployment costs. Despite the intermittency of power generation through sunlight, energy storage systems such as batteries enhance the stability and reliability of a standalone system, improving access to electricity. The PV systems' lack of moving parts and low maintenance compared to alternative generation sources such as diesel generators (DGs) or wind turbines are an added benefit to areas lacking access. With the global move towards utilizing cleaner and more sustainable energy sources, there is a solid motivation to deploy off-grid PV systems to replace traditional DGs. With the levelized cost of electricity as the primary key performance indicator, this paper compares different energy sources and configurations. This comparative case study first investigates the crucial elements, such as capital, operation, and maintenance expenditures. These factors can affect the cost competitiveness of off-grid PV systems in various settings. Further analysis points out key aspects that affect the PV systems' economic viability in different parts of the world.

The vast, remote rural areas in China have abundant renewable energy sources (RESs) that are not well utilized. Recent studies have advocated microgrids for flexible utilization of RESs like wind and solar energy, making them a vital solution for rural electrification. This paper performs techno-economic modeling and analysis of off-grid microgrids. Regarding the modeling, the system structure of an off-grid microgrid and the power output models of various generation units are first proposed. Next, a realistic economic cost model is developed based on power outputs, energy policies, and natural resources in rural areas. Then, the electricity consumption behavior of rural residents is elaborated, including typical household and agricultural electricity consumption behaviors. Regarding the analysis, three representative Chinese villages are first selected for case studies based on geological and geomorphological characteristics. Next, a Markov chain Monte Carlo-based method is advocated to generate renewable energy output data. Then, the suitable models and installation capacity for each generation unit are suggested, considering the village size, load characteristics, local policies, etc. Results verify the proposed methodology.

To online estimate the power system sensitivity matrix considering mitigating data saturation, a series of multivariable least-squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memory-fading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix online estimation using online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.

With the increasing amount and type of connected microgrids in the near-term future power networks, how to optimize the operation of a multi-microgrid system efficiently and reliably has become essential for taking full advantage of the complex systems. In this paper, we propose a multi-timescale coordinated optimization strategy for hybrid three-phase/single-phase multi-microgrids. With the consideration of the economy of microgrids and three-phase unbalance constraints of multi-microgrids, a strategy of collecting-distributing fuzzy modified adaptive particle swarm economic optimization is provided. In this day-ahead strategy, the system is constructed as a bi-layer rolling optimizing structure, where the lower layer sets objectives for economic optimization, and the upper layer balances constrained tie-line power of single-phase microgrids based on the optimized results obtained from lower layer. On this basis, by taking day-ahead optimized tie-line power as baseline values, we obtain the real-time power of energy storage systems using an improved nondominated sorting genetic algorithm, which achieves distributed real-time optimization for the multi-microgrid. Simulation results verify that the proposed strategy is feasible.