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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]

Abstract This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio. Highlights • Evolutionary algorithms play significant role in demand response implementation. • Aggregator-consumer matching via evolutionary algorithms improves mutual benefits. • Two-archive strategy improves mutation and crossover speed in differential evolution. [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]