Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

Naz, Aqdas; Javaid, Nadeem; Rasheed, Muhammad Babar; Haseeb, Abdul; Alhussein, Musaed; Aurangzeb, Khursheed

doi:10.3390/su11102763

Cited by 39 publications

(15 citation statements)

References 48 publications

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“…For neural networks and SVM, they obtained MAPE of 51 percent and 48 percent, respectively. Several other researchers proposed and evaluated several different effective models for prediction of electric loads [43]- [47]. Specifically, in [47], we proposed a Hybrid deep learning model which, is composed of convolutional layers and LSTM layers, where the focus has been on power load forecasting of individual energy customer.…”

Section: Literature Reviewmentioning

confidence: 99%

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

et al. 2021

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With rapid advancements in renewable energy sources, billing mechanism (AMI), and latest communication technologies, the traditional control networks are evolving towards wise, versatile and collaborative Smart Grids (SG). The short term power load forecasting of individual as well as group of similar energy customers is critical for effective operation and management of SG. Forecasting power load of individual as well as group of similar energy customers is challenging compared to aggregate load forecasting of a residential community. The main reason is the high volatility and uncertainty involved for the case of individual and group of similar energy customers. Several machine/deep learning models have been developed in the recent past for forecasting load of individual energy customers, but such explorations are ineffective due to the requirement of one trained model for every energy customer, which is practically not feasible. We plan to build a deep learning model using convolutional neural network (CNN) layers in pyramidal architecture for effective load forecasting for a group of similar energy-profile customers. Initially, we grouped a subset of energy customers from database of Smart Grid Smart City (SGSC) into clusters using DBSCAN approach. The CNN layers are used for extracting feature from historical load of each cluster. The extracted feature of similar energy-profile customers (grouped based on clustering) is combined to make training-databases for each cluster. We have used the power load data from SGSC project, which contain thousands of individual household energy customers data. The developed Pyramid-CNN model is trained based on these sets of databases. The trained model is evaluated on randomly selected customers from few clusters. We obtained significantly improved forecasting results for randomly selected user from different clusters. Our adapted strategy of clustering based model training resulted in upto 10 percent MAPE improvement for the energy customers. The essence of our work is that energy customers can be grouped into clusters and then representative model could be developed/trained, which can accurately forecast power load for individual energy-customer. This approach is highly feasible, as we do not need to train a model per energy customer and still achieve competitive forecasting results.

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Section: Literature Reviewmentioning

confidence: 99%

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

et al. 2021

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“…However, cost-efficient solutions are obtained at the expense of consumers' discomfort and increased peak-to-average ratio (PAR). A game theoretic home energy management system (HEMS) is proposed for energy consumption scheduling of residential buildings under DR pricing schemes to reduce PAR and electricity bill in [10], [11]. However, these studies do not consider the tradeoffs between the electricity bill and user-discomfort.…”

Section: Igamentioning

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

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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 .

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“…Finally, it is concluded in the game that highly reactive consumers can accept large-capacity PV power generation, which can improve the economy of the PV system with fewer batteries and meet the demand for electricity with the minimum cost, thus obtaining more benefits. In [66], a two-stage Stackelberg game is conducted for the optimization of storage capacity and PV power generation in microgrids.…”

Section: ) Demand Sidementioning

confidence: 99%

Application of Game Theory in Integrated Energy System Systems: A Review

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et al. 2020

IEEE Access

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With the rapid development of society, global energy is in short supply. China has put forward an integrated energy system focusing on interconnecting energy resources and harnessing their complementary advantages to address the energy crisis, thus no longer pursuing the production, transportation and supply of a single source of energy. In the continuous development of integrated energy systems, the elements of participation and interaction are becoming more complex. Game theory, which can effectively solve the problems arising from multi-agent trading, is naturally introduced to the integrated energy system. This paper provides a comprehensive overview of the introduction of game theory into the integrated energy system. First, the development of integrated energy is briefly introduced, game scenarios in integrated energy systems is proposed, and game scenarios considering the energy supply side, distribution network, demand side and common planning and dispatching problems in integrated energy systems are summarized. Secondly, main game theory models in the integrated energy system are summarized, such as cooperative game theory model, the non-cooperative game theory model and the Stackelberg game theory model. Finally, the future prospect and challenge for the application of game theory in integrated energy systems is proposed. The new game models are introduced to the integrated energy system, and a mixed game is considered to solve related problems. It is hoped that this work can serve as a reference for the researchers in this field. INDEX TERMS Integrated energy system, game theory, energy management, demand response.

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Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

Cited by 39 publications

References 48 publications

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

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

Application of Game Theory in Integrated Energy System Systems: A Review

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