Energy Demand Side Management within micro-grid networks enhanced by blockchain

Noor, Sana; Yang, Wentao; Guo, Miao; Dam, Koen H. van; Wang, Xiaonan

doi:10.1016/j.apenergy.2018.07.012

Cited by 358 publications

(209 citation statements)

References 30 publications

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“…Game theory approaches often involve bidding, but a bidding scheme for residential demand response can also be realized using other techniques, see, for example, [36]. Game theory approaches can be further combined with blockchain technologies to improve network security [37].…”

Section: A Methods With Load Factormentioning

confidence: 99%

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Chiu

Hsieh

Chen

2020

IEEE Trans. Ind. Inf.

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Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residential users and the load factor of the underlying grid. A multiobjective optimization problem (MOP) is formulated and Pareto optimality is adopted. Stochastic search methods of generating feasible values for decision variables are proposed. Theoretical analysis is performed to show that the proposed methods can effectively generate and preserve feasible points during the solution process, which comparable methods can hardly achieve. A multiobjective evolutionary algorithm is developed to solve the MOP, producing a Pareto optimal demand response (PODR) program. Simulations reveal that the proposed method outperforms the comparable methods in terms of energy costs while producing a satisfying load factor. The proposed PODR program is able to systematically balance the needs of the grid and residential users.

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Section: A Methods With Load Factormentioning

confidence: 99%

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Chiu

Hsieh

Chen

2020

IEEE Trans. Ind. Inf.

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“…This type of control required a heavy investment for the communication and control technologies for each user. The work in References [41,42], suggested another decentralized game theoretic approach to minimize the PAR and cost of consumer, and through blockchain energy trading is made transparent with smart contracts to overcome the stress on overall system. The PV system with storage elements is incorporated in the proposed model to facilitate a DSM program, so that the consumers can schedule their demands with a higher degree of freedom to maximize their comfort.…”

Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

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“…Paper Title: RP-8-Energy Demand Side Management within micro-grid networks enhanced by blockchain [22]. Problem Addressed: Challenges with managing supply and demand of energy.…”

Section: Quality Focus: Demand Side Managementmentioning

confidence: 99%

“…RP-7 [14]is all about proposing a blockchain-based architecture aimed at regulation of energy production and distribution. RP-8 [22] proposes a demand-side management model and performed a case study to demonstrate the value of their model. RP-12 [7] proposes a model for fraud detection in power grids and suggest some clustering algorithm without any simulation data or results.…”

Section: Maturitymentioning

confidence: 99%

A Survey of Blockchain-Based Solutions for Energy Industry

Megha

Lamptey

Salem

et al. 2020

Advances in Intelligent Systems and Computing

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The energy industry needs to shift to a new paradigm from its classical model of energy generation, distribution, and management. This shift is necessary to handle digitization, increased renewable energy generation, and to achieve goals of environmental sustainability. This shift has several challenges on its way and has been seen through research and development that blockchain which is one of the budding technology in this era could be suitable for addressing those challenges. This paper is aimed at the survey of all the research and development related to blockchain in the energy industry and uses a software engineering approach to categories all the existing work in several clusters such as challenges addressed, quality attribute promoted, the maturity level of the solutions, etc. This survey provides researchers in this field a well-defined categorization and insight into the existing work in this field from 3 different perspectives (challenges, quality attributes, maturity).

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Energy Demand Side Management within micro-grid networks enhanced by blockchain

Cited by 358 publications

References 30 publications

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

A Survey of Blockchain-Based Solutions for Energy Industry

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