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Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development. [ABSTRACT FROM AUTHOR]

In this paper, an optimal hybrid (wind and grid)-hydrogen energy system (H-HES) is proposed using multi-objective non-dominated sorting algorithm (NSGA-II) optimization. The H-HES consists of the main energy system; wind-energy system (W-ES), which supplies a proton exchange membrane (PEM) electrolyzer via an energy management system (EMS) and a rectifier. In addition, the grid-energy system (G-ES) is available to support the W-ES to meet the PEM electrolyzer's energy demand, and the EMS facilitates control between the W-ES and G-ES. The W-ES is modelled using wind data from Wind Atlas South Africa (WASA) for six Renewable Energy Development Zones (REDZs) in South Africa and their appropriate wind turbine models. The selection of appropriate wind turbine models is guided by the optimal wind turbine variables obtained from NSGA-II corresponding to the optimal H-HES model. The optimal H-HES model is developed using two objective functions: cost of electricity and efficiency, which are minimized and maximized respectively and evaluated using NSGA-II available in Pymoo framework. NSGA-II successfully converges to a Pareto front, and the best solution for the H-HES cost of electricity and efficiency for each wind REDZ is determined by compromise programming; a multi-criteria decision-making technique available in Pymoo. From the optimal cost of electricity and efficiency solutions, optimal variables are successfully obtained for optimal modelling of the H-HES for each wind REDZ. [ABSTRACT FROM AUTHOR]

Nowadays, the production of craft beer in microbreweries has become very popular all over the world. However, recent studies have demonstrated that the craft beer production process in microbreweries can be considered as energy intensive due to the fact that more to 8% of the production cost is allocated to thermal processes such as heating and cooling. Therefore, most microbreweries have been applying some Energy Efficiency actions to their production processes to decrease the amount of energy consumed and maximize the profits. In cases where the amount of energy consumed cannot be reduced using Energy Efficiency actions, Demand Response measures are implemented to reduce the cost of energy needed to supply the different processes involved. From the available literature, most of the studies based on Demand Response in the microbrewery sector have focused on the use of heating resources for the onsite energy generation to directly support the thermal processes. Very few published studies looked at the onsite "electricity" generation with small scale renewable energy sources and onsite energy storage to assist with energy cost reduction strategies. Therefore, this paper develops an optimal energy management model to minimize the energy cost of a microbrewery, under demand response, supplied with a grid-connected photovoltaic system with battery storage system. As a case study, a microbrewery in South Africa has been selected for simulation purposes. The detailed brewing process's load profile, the solar resource, the system components' specifications as well as the Time of Use energy cost structure has been used as input to the developed model with the aim of assessing and analyzing the technical and economic performance of the proposed system under the given operation conditions and constraints. The simulation results have shown that, as compared to supplying the microbrewery exclusively by the grid, the break-even point of the proposed supply option happens after 9.5 years of operation, corresponding to ZAR 398583.18 (USD 22592,09) cumulatively spent. For the considered 20 years' operation lifetime, the projected savings on the lifecycle cost is ZAR 603490.49 (USD 34206,44) or 40.8%. The result of the discounted payback period analysis indicated that the total investment cost may be recovered in 13.8 years. The microbrewery is selected as a case study just to highlight the fact that some processes are critical and cannot be shifted without compromising the quality of the final product. Therefore, the proposed hybrid system, the developed model and the optimization methodology can be applied to any load in different demand sectors (residential, commercial and industrial) implementing demand response measures in order to reduce their operation energy costs. • A microbrewery is subjected to demand response for energy cost reduction. • A grid-connected dual tracking PV with battery is proposed to support the microbrewery demand. • Optimal control approach is applied to the proposed system for energy management. • Results demonstrate the potential techno-economic benefits of the system for microbreweries. [ABSTRACT FROM AUTHOR]

Healthcare institutions consume large amounts of energy, ranking the second highest energy-intensive buildings in the commercial sector. Within developed countries, the energy consumption of healthcare institutions may account for up to 18% of the overall energy usage in commercial sectors. Within developing countries, such as South Africa, the energy consumption of healthcare institutions is observed to be a close second to the food service sector. Energy consumption of healthcare institutions per bed typically range from 43–92 kWh per day. In this paper, the largest energy consumers in South African healthcare institutions are identified and appropriate energy-efficiency (EE) initiatives are proposed, in terms of performance, operation, equipment and technology efficiency (POET). Two main thermal energy consumers are identified as heating, ventilation and air conditioning (HVAC) and water-heating systems. These systems are critical to patient health and may be classified as non-deferrable loads. Therefore, several initiatives are suggested to improve the energy efficiency and demand-side management capability of these systems. These initiatives are subdivided into different levels: the conceptual level, active level, technical and further improvement level, as defined in the POET framework. At each level, energy-efficiency initiatives are introduced based on potential energy savings and the effort required to achieve these savings. In addition, model predictive control (MPC) approaches are discussed and reviewed as part of the further improvement section. Average possible energy savings ranged from 50%–70% at the conceptual level, while energy savings of 15%–30% may be expected for energy-efficiency initiatives at the active level. EE activities at the technical level and the further improvement level may result in savings of 50%–70% and 5%–10%, respectively. [ABSTRACT FROM AUTHOR]