Planning and optimization of standalone microgrid with renewable resources and energy storage

Abstract

Renewable energy has shown to be a feasible and cost‐effective approach to electrifying rural villages while also meeting the region's long‐term development goals. Due to the intermittent and unpredictable nature of renewable energy supply and the need to satisfy variable demand across time, capacity planning for a sustainable energy system is problematic. Because the energy planning problem is non‐linear and non‐convex, an efficient technique must be used to build a cost‐effective energy system that supplies reliable electricity to the area. In the present work, a rural microgrid is suggested for the isolated community of the Indian hilly region. The integrated model combines existing energy resources in the area. The microgrid consists of the solar photovoltaic system, micro‐hydropower, wind energy biomass, and storage foreconomical and reliable electrification. The modeling of microgrids is based on the availability of renewable resources. Using the Differential Evolution optimization algorithm, the recommended hybrid energy model examined the per‐unit cost of energy and the overall operating cost. Based on the simulation for a year, it has been found that the optimal system has 35 kWof biomass, 30 kW of biogas, 40 kW of MHP, 144 kW of SPV, and 42 kW of the wind energy system to satisfy the energy demand of the area entirely. The per‐unit cost of energy is 0.12$/kWh, and the total operating cost is $ $2 120 700, respectively. Further, the sensitivity analysis is performed to know the impact of change in parameters on energy cost. The suggested microgrid provides a cost‐effective solution to the community.

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

“…[3][4][5][6][7] The utilization of energy storage systems (ESS) and traditional standby power sources in off-grid systems help mitigate the consequences of these sources' inherent volatility. [8][9][10][11] The ESS used in the off-grid systems adds a substantial cost and for this reason, it is important to have smart demand-side management (DSM) to reduce loads during times of scarcity while urging consumers to raise their loads in case of higher generation than the loads need periods which increases the fit between loads and renewable energy generation. [8][9][10][11][12][13][14][15] The smart DSM and ESS can synergistically work together to significantly reduce energy cost and improve reliability.…”

“…Besides, the use of ESS or CGs, the use of DMS added substantial improvements to the HRES in terms of cost and reliability. [8][9][10][11][12][13][14][15][16][17][18][19][20] Several ESS technologies have been used with HRES where each type may be suitable for a certain size and operating conditions of the HRES and they may be unsuitable for others. The ESS can be classed according to the technique utilized as electrochemical or battery energy storage systems (BESS), chemical storage, mechanical storage, electrical storage, or thermal storage.…”

“…Because of the BESS's quick ramp rate and high efficiency, it has been employed with most HRES alone or in combination with other forms of ESS. [8][9][10][11][12][13][14][15][16][17][18][19][20] The mechanical ESS are converting the mechanical to electrical energy and vice versa. In the literature, several forms of mechanical storage systems are employed, including pumped hydro energy storage systems (PHES), 13 liquid air ESS (LAES), compressed air energy storage (CAES), and flywheel.…”

“…Several studies used the DSM in the design stage of the HRES for the same purpose. [8][9][10][11][12][13][14][15][16][17][18][19][20] Some studies introduced a demand response (DR) technique via a relationship between the change of tariff and the battery's state of charge (SoC). [13][14][15]25 This relation is determining the required change of tariff according to the SoC and the BESS power.…”

“…[31] FLC has been introduced to regulate the optimal electrolyzer/fuel cell operation according to the discrepancy between RESgenerated production and load demand. Besides the FLC, there are other strategies used in determining the optimal tariff for the HRES such as a multi-agent system, 32 linear programming approach 10,11 model predictive control, 33 and rule-based approach. 34 A significant reduction in the system size, the system's reliability, energy cost, and GHG emissions will be the result of all the advantages gained through the DSM application during the sizing and operation of the HRESs.…”