This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.

Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints

A review of wind speed and wind power forecasting with deep neural networks

Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load forecasting for individual residential customers plays a progressively crucial role in the operation and planning of future grids. Compared to the aggregated electrical load at the community level, the prediction of individual household electric loads is legitimately challenging because of the high uncertainty and volatility involved. Results from previous studies show that prediction using machine learning and deep learning models is far from accurate, and there is still room for improvement. We herein propose a deep learning framework based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM). The proposed hybrid CNN-LSTM model uses CNN layers for feature extraction from the input data with LSTM layers for sequence learning. The performance of our developed framework is comprehensively compared to state-of-the-art systems currently in use for short-term individual household electric load forecasting. The proposed model achieved significantly better results compared to other competing techniques. We evaluated our proposed model with the recently explored LSTM-based deep learning model on a publicly available electrical load data of individual household customers from the Smart Grid Smart City (SGSC) project. We obtained an average mean absolute percentage error (MAPE) of 40.38% for individual household electric load forecasts in comparison with the LSTM-based model that obtained an average MAPE of 44.06%. Furthermore, we evaluated the effectiveness of the proposed model on different time horizons (up to 3 h ahead). Compared to the recently developed LSTM-based model tested on the same dataset, we obtained 4.01%, 4.76%, and 5.98% improvement for one, two, and six look-forward time steps, respectively (with 2 lookback time steps). Additionally, we have performed clustering analysis based on the power consumption behavior of the energy users, which indicate that prediction accuracy could be improved by grouping and training the representative model using large amount of data. The results indicated that the proposed model outperforms the LSTM-based model for both 1 h ahead and 3 h ahead in forecasting individual household electric loads.

/pdf/hybrid-cnn-lstm-model-for-short-term-individual-household-2f3wcwx8qx.pdf

Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid

In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.

https://www.mdpi.com/2071-1050/11/24/7054/pdf

A Secure Data Sharing Platform Using Blockchain and Interplanetary File System

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce the energy consumption cost, emission of harmful gases (as renewable energy sources are used to generate energy at user's premises) and increase the smart grid resilience. However, local energy trading with peers can have trust and privacy issues. A centralized system can be used to manage this energy trading but it increases the overall cost of the system and also faces several issues. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based system is proposed. It is fully decentralized and allows the market members to interact with each other and trade energy without involving a third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with the main smart contract, which requests P2P and prosumer to grid smart contracts for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio of electricity.

/pdf/a-blockchain-based-load-balancing-in-decentralized-hybrid-11wurn2xvn.pdf

A Blockchain-Based Load Balancing in Decentralized Hybrid P2P Energy Trading Market in Smart Grid

Fuzzy energy management controller and scheduler for smart homes

A survey on hyperparameters optimization algorithms of forecasting models in smart grid

The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks. Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties. To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities. Smart contracts are used to tackle trading disputes and illegal actions. Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain. Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup. Removing data duplication at an earlier stage reduces storage cost. Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data. To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.

/pdf/blockchain-based-data-and-energy-trading-in-internet-of-1pi72s9pxz.pdf

Rabiya Khalid

Papers

A Secure Data Sharing Platform Using Blockchain and Interplanetary File System

A Blockchain-Based Load Balancing in Decentralized Hybrid P2P Energy Trading Market in Smart Grid

Fuzzy energy management controller and scheduler for smart homes

A survey on hyperparameters optimization algorithms of forecasting models in smart grid

Blockchain Based Data and Energy Trading in Internet of Electric Vehicles