Different Storage-Focused PV-Based Mini-Grid Architectures for Rural Developing Communities

Abstract

Impacts of grid architectures on temporal diffusion of PV-based communal grids (community owned minigrids or microgrids) in a rural developing community are modelled and simulated using MATLAB/Simulink and a survey-informed agent-based model (ABM) developed in NetLogo. Results show that decentralised control architectures stimulate minigrid formations and connections by allowing easy expansions of the minigrids as each decentralised PV system within a minigrid is treated equally and determines its own real and reactive power, eliminating the need for communication links. This also reduces the cost of implementing such a system; fewer connections are realized with centralised controls as such systems require high speed communication links which make them both difficult to expand and expensive to implement. Results also show that multi-master operation modes lead to more communal grid connections compared to single-master operation modes because in the former, all distributed PV systems within a communal grid have the same rank and can act as masters or can be operated as combinations of master generators (VSIs) and PQ inverters, allowing for more design flexibility and easy connections from potential customers.

How solar home systems temporally stimulate increasing power demands in rural households of Sub-Saharan Africa

“…1 [9]. Furthermore, nearby households with SHS can network their systems into small communal microgrids or regional grids, with centralized or decentralized storage, leading to access to grid-level power and thus to improved reliability and availability of power, to improved system security, and to improved overall system efficiency [9,10].…”

Simulation and Analysis Approaches to Microgrid Systems Design: Emerging Trends and Sustainability Framework Application

“…Planning Dimension Software/Technique Technical and economic design, modelling and performance analysis of microgrid systems based on renewable and non-renewable resources and storage TE MILP [10]; HOMER/ANN-BP and LM [11]; optimal and sensitivity analysis [13]; HOMER/comparative analysis [14,15]; MANGO [16]; HOMER/integrated analysis [18]; modified non-dominated sorting GA [19]; MATLAB/MILP [20]; mixed integer MOO [22]; GA [23]; HOMER [24]; Monte Carlo simulation [25]; HOMER/PSO [26]; HOMER/TOPSIS [27]; HOMER/MATLAB [28]; GA [32]; HOMER/MATLAB [33]; HOMER/MATLAB/DSM [34]; HOHER RETScreen [35]; CPLEX [37], MATLAB/MPM-GAMS [40]; MOO [42] Technical, economic and environmental design, simulation and performance analysis of microgrid system with storage TEE MILP [10]; Eco-SIM [31]; RETScreen [36]; AHP-MCDM [38] Social, technical and economic design, modelling and analysis of microgrid systems STE GA, PSO and TLBO [12]; DIgSILENT PowerFactory, HOMER/integrated analysis [21] Technical design, modelling and performance analysis of microgrid systems T GA and Python optimal control problem [29]; MOO and MCDM [30]; fuzzy logic control strategy [39]; MATLAB Simulink/agent-based model [41]; MATLAB and HOMER [43], IoT-based approach [44] The study introduces a practical simulation framework for sustainable energy planning, which is based on the social-technical-economic-environmental-policy (STEEP) model. This model is comprehensive than the other models such as TE, ST, STE, TEE or T planning dimensions.…”