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Globally energy management has become a challenge and concern due to rapid increase in energy demand and energy security. In South Africa, electricity supply problems has resulted in load shedding and steep increase in electricity cost. The objective of this paper is to optimise the residential electricity consumption by scheduling the household appliances in line with the time of use (TOU) tariffs. This was done through a fuzzy logic electrical energy controller. The optimisation was achieved by varying load consumption per hour. The fuzzy rules were designed to make intelligent decisions considering the time of use tariff, the remaining daily limit and load consumption. The preferred number of days were set by the consumer and the system calculated the daily limit considering the available amount of electricity in smart meter. The findings showed that the fuzzy logic optimised the residential electricity consumption by scheduling the household appliances in line with the time of use (TOU) tariffs. The fuzzy logic may provide a way in which the smart meter could optimise and enhance the electricity user consumption. [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]

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]