The incorporation of Distributed Energy Resources (DER), especially PV and BESS, is essential in making radial power systems stable. Unfortunately, most recent optimization algorithms make an assumption of an unconstrained utility supply, targeting minimization of losses during daylight or only profit-making energy trading. Such traditional methods will not work in underdeveloped areas that face significant capacity limitations in their main substations, forcing them to undertake mandatory load shedding. In such situations, having an unconstrained BESS leads to parasitic voltage drop at weak tail nodes, whereas reactive disconnect switches lead to excessive Energy Not Served (ENS) production. In order to fill this gap, an artificial intelligence-based approach of survivability control that changes the focus from economic dispatching to active grid survivability has been proposed in this study. By using Particle Swarm Optimization (PSO), the proposed approach incorporates modelling of artificial solar intermittency, constrained and asymmetric dispatching of BESS systems, and a demand-side management procedure. A penalization technique is used to ensure strict adherence to the statutory voltage constraints. Using the IEEE 33 bus network system with the maximum capacity for active power at 4.0 MW, the AI-based approach performed significantly better compared to the conventional distributed generation strategies. The new proposed approach was able to ensure that there were no violations of the absolute minimum network voltage (0.95 p.u.), reduced power losses to 1.696 MWh (a 31.6% improvement over static DG methodologies), and prevented all instances of unexpected Energy Not Served (ENS).
T. Somefun, S. A. Aransiola, Tshepo Cameron Modisane et al.· Journal of Electrical System...· 0 citations
The transition toward reliable and sustainable microgrids in constrained power systems requires both accurate load demand forecasting and the intelligent resource coordination. The primary objective of this study is to optimize the capacity sizing and operational resilience of such systems by proposing an Artificial Intelligence-driven Load Forecasting and Battery-Integrated Energy Management approach, structured around a DC-coupled solar-plus-storage (DC-CS-P-S) architecture. To accurately capture the complex thermodynamic and behavioural patterns driving electricity consumption, a multivariate long short-term memory (LSTM) network was developed. The proposed strategy physically decouples energy storage recovery from the grid by dedicating solar photovoltaic generation strictly to charging the battery energy storage system to ensure availability for peak shaving and essential load protection. Evaluated over an 8760-hour simulation, the LSTM forecasting achieved high predictive accuracy with a mean absolute percentage error of 3.31% and an RMSE of 12.39 kW. Under severe bottleneck constraints, the DC-coupled battery actively contributed 93.64 kW of peak shaving power, successfully diverting 100% of the energy deficit (135 020 kWh) entirely to non-essential infrastructure. Furthermore, a comprehensive techno-economic parametric analysis, incorporating a value of lost load reliability penalty, and a $50/ton carbon price, identified 330 kW as the absolute optimal grid capacity. At this threshold, the microgrid achieves complete reliability with zero energy shedding while maintaining a minimized baseline operational costs of $92 033 USD. Ultimately, the results demonstrate that intelligently maximizing zero-emission resources actively suppresses compounding carbon liabilities (averting a 21.6% financial premium), providing system planners with a resilient, data-driven methodology to optimize infrastructure investments without over-sizing centralized grid connections.
T. Somefun, Esenogho Ebenezer· Engineering Research Express· 0 citations
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