Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

Jiang, Xin; Xiao, Chunyan

doi:10.1109/access.2019.2928374

Cited by 63 publications

(31 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HEMC is based on our proposed DA-GmEDE and existing (DA-GA, DA-game-theoretic) strategies. Our proposed DA-GmEDE based scheduling strategy is compared with W/O (without) scheduling and scheduling based on existing strategies: DA-GA [18], [19] and DA-game-theoretic [37] to validate the superiority of the proposed strategy. For fair comparison, we have used day-ahead forecasted DR pricing signal and energy consumption pattern, and the same set of appliances as listed in Table 3 for our proposed DA-GmEDE strategy and existing strategies (DA-GA, and DA-game theoretic).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

et al. 2020

View full text Add to dashboard Cite

In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and dayahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management. INDEX TERMS Advanced metering infrastructure, artificial neural networks, demand response, energy management, grey wolf modified enhanced differential evolution algorithm, smart grid. NOMENCLATURE SG Smart grid ANN Artificial neural network HEMS Home energy management system MILP Mixed integer linear programming BBSA Binary backtracking search algorithm The associate editor coordinating the review of this manuscript and approving it for publication was Behnam Mohammadi-Ivatloo .

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Many relevant studies have been conducted to improve the energy management and optimize the costs. Various techniques, such as fuzzy logic [63], cooperative game theory [64], genetic algorithms [65], and deep recurrent neural networks [66], have been proposed. Specifically, a reinforcement learning-based strategy (Fuzzy Q-learning) was recently proposed to improve the decision-making process of P2P power trading [67].…”

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Son

Kwon

et al. 2020

Energies

View full text Add to dashboard Cite

Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.

show abstract

“…In [5], the authors proposed GA and compared its results with WDO. The results showed a 29% reduction in the EC and a 36.2% reduction in the PAR.…”

Section: Related Workmentioning

confidence: 99%

“…Also, the gravitational force pulls the air particles towards the origin, while the Coriolis force's function is to deflect the air particles in the atmosphere. Equations (3), (4), (5), and (6) are used to calculate the pressure gradient force, Coriolis force, gravitational force , and friction force, respectively. All these forces can be mathematically written as [36]:…”

Section: B Bpso Algorithmmentioning

confidence: 99%

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Imran¹,

Hafeez²,

Khan³

et al. 2020

IEEE Access

103

View full text Add to dashboard Cite

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

show abstract

Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

Cited by 63 publications

References 15 publications

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Contact Info

Product

Resources

About