Price-based demand response for household load management with interval uncertainty

Judge, Malik Ali; Manzoor, Awais; Maple, Carsten; Rodrigues, Joel J. P. C.; Islam, Saif ul

doi:10.1016/j.egyr.2021.02.064

Cited by 46 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We designed a forecasting model for solar energy prediction to ensure more efficient energy management. The exogenous signals like load, temperature, solar irradiance, and RTPDR are forecasted using ANN, which are shown in Figures 5, 6, 7, and 8, respectively [44]. The load pattern depicted in Figure 5 is the energy consumption profile of a smart home having three types load: elastic, noninterruptible, and rechargeable, which is forecasted using ANN.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Their objectives were operation cost, pollution emission, and availability optimization. The authors in [20], assumed that consumer is provided with a smart meter which has an energy consumption controller (ECC) unit. This ECC units are via LAN connected with neighbors for sharing power utilizing information.…”

Section: Relevant Literature Surveymentioning

confidence: 99%

“…The objective function is dependent on the minimum energy cost, PAR, and the constraints given in Eqs. (20) to (26). The objective function is given below:…”

Section: E Desired Objectives Functionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

et al. 2021

View full text Add to dashboard Cite

The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.

show abstract

Section: Simulation and Resultsmentioning

confidence: 99%

Section: Relevant Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, they have ignored RESs integration in their work. The authors in [8] suggested the techniques of Harris' Hawk optimization combined with integer linear programming (ILP) to solve the randomness problem and to schedule the user appliances. Authors' main objectives were to analyze cost and the trade-off strategies for user comfort and financial benefits.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

et al. 2021

View full text Add to dashboard Cite

Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User's electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO 2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively. INDEX TERMS Energy management; battery energy storage systems; renewable; hybrid heuristic algorithms; power usage scheduling; smart grid.

show abstract

“…Point prediction offers only one simple value [8], and in contrast, probability prediction provides quantile intervals to quantify uncertainties for short-term price fluctuation [9,10]. In practice, the target of electricity price forecasting has no exact requirement to point predication, but prespecified price thresholds served in the process of decision instead, such as DR, which gives more crucial significance to commercial decisions in the electricity market [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting

Deng

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.

show abstract

Price-based demand response for household load management with interval uncertainty

Cited by 46 publications

References 39 publications

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting

Contact Info

Product

Resources

About