Citation: Philipo, G.H.; Kakande, J.N.; Krauter, S. Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies 2022, 15, 5215. https:// doi.org/10.3390/en15145215 Academic Editors: Alon Kuperman and Agnieszka Rzepka Received: 25 May 2022 Accepted: 13 July 2022 Published: 19 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping Godiana Hagile Philipo 1,2, * , Josephine Nakato Kakande 1,3 and Stefan Krauter 1 1 Electrical Energy Technology—Sustainable Energy Concepts, Faculty of Computer Science, Electrical Engineering and Mathematics, Paderborn University, Pohlweg 55, D-33098 Paderborn, Germany; jkakande@mail.uni-paderborn.de (J.N.K.); stefan.krauter@uni-paderborn.de (S.K.) 2 Department of Material, Energy, Water and Environmental Sciences, The Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania 3 Department of Electrical and Computer Engineering, CEDAT, Makerere University, Kampala P.O. Box 7062, Uganda * Correspondence: philipog@campus.uni-paderborn.de Abstract: Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers. Keywords: microgrid; neural network; demand response; energy storage; smart grid; demand-side management; load shifting 1. Introduction Undoubtedly, the availability, acceptability, efficiency and affordability of energy are fundamental to advanced civilization and better quality of life [1,2]. Energy access is essential in achieving healthy and productive households with a growing modern economy [3]. Several reports advocate that the use of electricity by rural communities in developing countries could be advantageous to its inhabitants, especially for services related to water [4], agriculture, health, education and commerce [5,6]. Poor access to electricity in rural areas has been linked to community development gaps, leading to rural to urban migration and putting further stress on already strained urban infrastructure systems [7–9]. However, in Sub-Saharan African countries, only about two-fifths of the population has access to electricity, the lowest proportion in the world [3,10]. In East Africa, the situation is still critical, since about 80% of the population has no or unreliable access to electricity, the majority being people in rural areas [11,12]. Rural area electrification in developing countries poses challenges in constructing power generation and transmission networks [6]. Several studies have depicted that, despite the political efforts to improve grid transmission and power generation, emphasis is more on the urban and industrial loads due to their higher demand [13] and political relevance. Energies 2022, 15, 5215. https://doi.org/10.3390/en15145215 https://www.mdpi.com/journal/energies