Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid

Abstract

With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization.

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“…In contrast to Albogamy et al (2022), Almutairi et al (2022), Liu et al (2022), Saxena et al (2022), Zaidan et al (2022), it was found that Smart Grid mainly depends on government governance, which includes both infrastructure support, and regulation of the energy economy. The role of corporate governance is secondary.…”

“…The publications of Albogamy et al (2022), Almutairi et al (2022), Liu et al (2022), Saxena et al (2022), Zaidan et al (2022) suggest the functioning and development of Smart Grid based on corporate governance with a secondary role of the state. Smart Grid is interpreted as a high-tech innovation of energy companies.…”

Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

“…Similarly, in [48], authors developed an optimal scheduling scheme for home appliances' power usage pattern management to mitigate PAR and alleviate appliances' waiting time. However, carbon emission and energy cost minimization is ignored.…”

“…In [50], the authors introduced Grey Wolf Accretive Satisfaction Algorithm for DSM to ensure the lowest energy cost and highest user comfort. A Candidate Solution Updating Algorithm (CSUA) is introduced in [48]. The purpose is to minimize PAR and appliance waiting time subjected to the desired user comfort level.…”

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“…To encourage consumers, a real-time pricing (RTP) and demand response (DR) program are mathematically modelled and implemented to solve energy management problems via scheduling residential loads. To solve the energy management problem, a hybrid algorithm using an enhanced differential evolution (EDE) and a genetic algorithm (GA) were used by the authors of [32]. Luis Corona et al [33] presented a model to build and identify the main drivers of price convergence in Central-Western Europe (CWE) after applying the complex flow-based market coupling (FBMC) mechanism based on publicly available data.…”