An integrated Dynamic Pricing Scheme for improving the smartness of off grid distributed generation

This paper presents an integrated dynamic pricing scheme (iDPS) developed for an off grid community in sub-Sahara Africa (SSA). This proposed model employs a neighbourhood approach in effectively determining the electricity units due to connected consumers based on their monthly contributions. Assuming base values, this model obviates the need for real time inputs from the users owing to the high illiteracy level in SSA and dynamically computes electricity price in real time such that below par paying consumers pay more compared to base or above base consumers. Additionally, the smart load distribution board employed ensures that electricity units are dispatched in quantized values demanding greater energy efficiency from the consumers. This model achieves economic accrual which guarantees the sustainability of the off grid DG project

Variable Weighted Multi-Objective Multi-Dimensional Genetic Algorithm for Demand Response Scheduling in a Smart Grid

This research presents the optimized scheduling of demand response loads of a residential community of 30 houses using a multi-objective multi-dimensional genetic algorithm (MOMD-GA) with a variable weighted objective function. Incorporating day ahead hourly real time pricing (RTP), the MOMD-GA attempts to present possible optimized dispatch patterns with their associated penalties and constraints (environmental, consumers and suppliers) thus providing system operators (SOs) and distribution network operators (DNOs) sufficient data for real time decision making. The variable weights for each considered component of the cost function is chosen to force the MOMD-GA towards exploring optimum solutions with lower environmental cost. Further shown are the trade-offs in selecting particular dispatch bias (consumer, supplier, environmental and optimized) and the impact of the various dispatch scenarios on the cost of overall electricity bill of the community