• Energy Research

Dynamic pricing can be considered a key method for the smart grid which maintains a balance supply and demand with the help of a closed-loop super twisting sliding mode controller (STSMC). Demand-side load management (DSLM) consists of agents that control, change, shift and modify the load usage pattern according to dynamic pricing offered by the smart grid. The DSLM agents under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. A control theoretic approach is introduced in this work for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated micro grid platform is discussed to show that the demand of consumers can be controlled through STSMC which regulates the electricity price to the DSLM agents installed in smart meters at the demand side. The overall demand elasticity of the current study is represented by a first order dynamic price generation model having a piece-wise linear DR program and uncertainty of 1MWh is added in overall demand response of the system to make simulations more realistic. The simulations validate that STSMC is a useful technique to control closed loop elastic demand and preserve a balance between demand and generation in renewable energy integrated micro grid.

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

Energy balancing in smart microgrid plays a vital role to improve the reliability and resolves the load shedding problem to ensure consistent energy supply. However, energy balancing is challenging due to uncertain and intermittent nature of renewable energy integrated in smart microgrid. To solve such problems, dynamic energy pricing mechanism is developed that maintain energy balance for overcoming the gap between demand and supply. Thus, the particle swarm optimization based super twisting sliding mode controller (PSO-STSMC) is developed which uses dynamic energy pricing to control renewable energy resources’ generation according to the consumers’ demand for real time closed loop energy balancing in an energy market. The proposed PSO-STSMC based model is compared with existing models like proportional integral derivative (PID) controller, proportional integral (PI) controller, proportional derivative (PD) controller, and fractional order proportional derivative (FO-PD) controller and the optimized models of the particle swarm optimization based proportional integral (PSO-PI) controller and particle swarm optimization based proportional integral derivative (PSO-PID) controller. Simulations results demonstrate that energy price regulation by PSO-STSMC consistently controls the elastic demand for real time energy balancing.

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

A real-time energy management strategy using dynamic pricing mechanism by deploying a fractional order super twisting sliding mode controller (FOSTSMC) is proposed for correspondence between energy users and providers. This framework, which controls the energy demand of the smart grid’s users is managed by the pricing signal provided by the FOSTSMC, issued to the smart meters, and adjusts the users’ demand to remove the difference between energy demand and generation. For the implementation purpose, a scenario based in MATLAB/Simulink is constructed where a sample renewable energy–integrated smart microgrid is considered. For the validation of the framework, the results of FOSTSMC are compared with the benchmark PI controller’s response. The results of the benchmark PI controller are firstly compared in step response analysis, which is followed by the comparison in deploying in renewable energy–integrated smart grid scenario with multiple users. The results indicate that the FOSTSMC-based controller strategy outperformed the existing PI controller-based strategy in terms of overshoot, energy balance, and energy price regulation.