Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given.

Modelling and optimisation of biogas production from different substrate mixtures is lagging behind in research and development. Current biogas production processes are not fully exploiting co-digestion of multifaceted biomaterials with manures and other biowastes. A model is presented for the determination of biogas production potential from water hyacinth (WH), municipal solid waste (MSW) and cow dung (CD) as well as the subsequent optimisation of the co-digestion mix ratios of these substrates. In this study biogas is assumed to comprise of methane, carbon dioxide, ammonia and hydrogen sulphide. Baseline biogas potential yields of 747.4 ml/gVS, 790.83 ml/gVS and 884.24 ml/gVS were obtained from WH, MSW and CD, respectively. A linear programming mathematical optimisation was done. The objective is to find substrate blend ratios in the co-digestion mixture that maximises biogas production. Optimal co-digestion results in percentage substrate blending ratios of $53.27:24.64:22.09$ for WH, MSW and CD, respectively in a case study. One kilogram of substrate mixture yields $124.56{\mathrm{}\mathrm{m}}^3$ of biogas which translates to 124,560 ml/gVS. Co-digestion and optimisation of substrate blend mix proportions increased the biogas output by $157.11\%.$ The biogas fratenity benefits in having an informed optimal co-digestion model that foretells substrate blending ratios.

Users ought to decide how to operate and manage power systems in order to achieve various goals. As a result, many strategies have been developed to aid in this regard. Optimal power flow management is one such strategy that assists users in properly operating and managing the supply and demand of power in an optimal way under specified constraints. However, in-depth research on optimal power flow management is yet to be explored when it comes to the supply and demand of power for the bulk of standalone renewable energy systems such as solar photovoltaics, especially when it comes to specific applications such as powering military soldier-level portable electronic devices. This paper presents an optimal power flow management modelling and optimisation approach for solar-powered soldier-level portable electronic devices. The OPTI toolbox in MATLAB is used to solve the formulated nonlinear optimal power flow management problem using SCIP as the solver. A globally optimal solution was arrived at in a case study in which the objective function was to minimise the difference between the power supplied to the portable electronic device electronics and the respective portable electronic device power demands. This ensured that the demand for solar-powered soldier-level portable electronic devices is met at all times in spite of the prohibitive case scenarios’ circumstances under the given constraints. This resolute approach underscores the importance placed on satisfying the demand needs of the specific devices while navigating and addressing the limitations posed by the existing conditions or constraints. Soldiers and the solar photovoltaic user fraternity at large will benefit from this work as they will be guided on how to optimally manage their power systems’ supply and demand scenarios. The model developed herein is applicable to any demand profile and any number of portable electronic device and is adaptable to any geographical location receiving any amount of solar radiation.

Efficiently managing and prioritising the charging of portable electronic devices powered by solar photovoltaic sources in off-grid and resource-limited environments is a huge problem. Ensuring that critical devices maintain operational uptime, especially when energy resources are scarce and in instances where multiple devices compete for charging from the limited solar power available is crucial. This paper introduces an optimisation framework designed to prioritise the charging of portable electronic devices powered by solar photovoltaic sources. The approach aims to maximise operational uptime for critical loads before addressing less essential ones. By strategically allocating charging priorities based on comprehensive evaluations of battery capacities, usage patterns, and operational requirements, the optimisation process seeks to enhance overall efficiency and readiness of portable electronic devices in dynamic, austere and resource-constrained settings. The charging prioritisation problem was solved using MATLAB’s (version number 9.13.0.2193358 (R2022b)) OPTI toolbox in conjuction with the SCIP solver. A case study, involving three portable electronic devices—a cellphone, GPS and radio, demonstrated the model’s effectiveness in maximising satisfaction by aggregating device priorities over time. The model prioritised charging of the GPS due to its critical operational role, followed by the radio for its essential communication function, while the cellphone, with lower usage demands, was assigned the lowest priority. The model developed in this study is versatile and applicable to diverse demand profiles and any number of portable electronic devices. Furthermore, it can be customised to operate effectively in various geographic locations, irrespective of solar radiation levels.