An optimal P2P energy trading model for smart homes in the smart grid

Alam, Muhammad Raisul; St-Hilaire, Marc; Kunz, Thomas

doi:10.1007/s12053-017-9532-5

Cited by 112 publications

(62 citation statements)

References 35 publications

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“…From the perspective of prosumers, cost optimization is widely reported as a major motivation for bilateral TE among peers [36][37][38][39][40][41][42]. Cost optimization can be achieved through reductions in generation costs, transport costs, energy demand or through profit maximization.…”

Section: Cost Optimization and Economicsmentioning

confidence: 99%

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State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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Abstract:Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.

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Section: Cost Optimization and Economicsmentioning

confidence: 99%

“…Furthermore, Ref. [36] addressed P2P energy trading among smart grid households with the aim of optimizing cost by considering components with a significant impact on cost such as storage and renewable resources. The smart homes in their model are connected through a bi-directional distribution network.…”

Section: Classification Based On Distributed Structurementioning

confidence: 99%

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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“…There are several studies related to P2P energy trading system. [2] Applied P2P energy trading to community MG, [3] created a P2P model for smart homes in a smart grid, [4] implemented a P2P model to apply information and communications technology to local smart grids and control the grid, [5] confirmed that P2P energy trading could actually be done on low voltage electrical distribution networks, [6] investigated and compared current energy transactions, and [7] applied a blockchain technology to P2P energy trading. In fact, if you trade energy in a MG, there will be several losses.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Energy Loss over Distance and Activating the Energy Trading System in Microgrid

et al. 2019

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As small-scale distributed energy is gradually expanding, commercialization of peer to peer(P2P) energy trading that freely exchanges energy among individuals in various countries is being commercialized, and the Microgrids (MGs) are considered to be an optimal platform for P2P energy trading. Although conducting electricity trade among individuals without going through power companies is still in its infancy, it is expected to expand gradually as the awareness of the shared economy grows and the MG spreads. Research on more efficient trading systems is needed while trading energy in MG. Therefore we propose a more efficient energy trading system that minimizes the loss in proportion to the distance of the power line when energy trading is performed in the MG. We have constructed a virtual MG environment and experimented with energy trading scenarios. As a result, when the algorithm is applied, loss in proportion to the distance is reduced by 2.495% and energy trading becomes more active. The amount of energy and the number of trades increased by 1.5 times during the energy trading process.

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“…Exact solvers such CPLEX and GAMS which implement branch-and-bound or cutting planes algorithms are suitable for small sizedproblems for finding optimal solutions within a reasonable computation time. However, when the number of integer and/or binary variables grows in medium and large MILP problems, these algorithms are extremely time-consuming and have exponential computing complexity [9]. To use an exact solver in less time, some existing works proposed an integer-free optimization model for energy management in order to reduce the computation time [10].…”

mentioning

confidence: 99%

“…However, this work ignores the scheduling of on/off status of appliances.Classical techniques for dealing with multi-objective optimization problems convert the multiobjective optimization problem (MOOP) to a single-objective optimization problem (SOOP). The epsilonconstraint method involves optimizing one objective and use all other objectives as constraints [9,11], the WSA involves optimizing a weighted sum of various objectives [12], the weighted metric methods indicate optimizing an lp metric constructed from all objectives [13], and the goal programming technique involves optimizing a weighted sum of deviations of objectives from user-specified targets [14]. The arised SOOP should be tackled using a single-optimization algorithm.…”

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confidence: 99%

A hybrid non-dominated sorting genetic algorithm for a multi-objective demand-side management problem in a smart building

Garroussi¹,

Ellaia²,

Talbi³

et al. 2020

IJECE

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One of the most significant challenges facing optimization models for the demand-side management (DSM) is obtaining feasible solutions in a shorter time. In this paper, the DSM is formulated in a smart building as a linear constrained multi-objective optimization model to schedule both electrical and thermal loads over one day. Two objectives are considered, energy cost and discomfort caused by allowing flexibility of loads within an acceptable comfort range. To solve this problem, an integrative matheuristic is proposed by combining a multi-objective evolutionary algorithm as a master level with an exact solver as a slave level. To cope with the non-triviality of feasible solutions representation and NP-hardness of our optimization model, in this approach discrete decision variables are encoded as partial chromosomes and the continuous decision variables are determined optimally by an exact solver. This matheuristic is relevant for dealing with the constraints of our optimization model. To validate the performance of our approach, a number of simulations are performed and compared with the goal programming under various scenarios of cold and hot weather conditions. It turns out that our approach outperforms the goal programming with respect to some comparison metrics including the hypervolume difference, epsilon indicator, number of the Pareto solutions found, and computational time metrics. 560 or other forms of financial incentives. For instance, in a smart building, the smart meter receives external price signal such as time of use pricing (ToU) which vary depending on three periods, off-peak, mid-peak and on-peak price periods, and then all connected energy devices are scheduled to give a better economic planning, but affecting consumer preferences, so that it might be operated at undesired periods. However, in such circumstances, considering a trade-off between the economic targets of the DSM and a convenient lifestyle is substantial. The automatic implementation of the DSM scheduling model in a smart building (B-DSM) is performed by using a home/building energy management system (HEMS/BEMS) and is becoming a challenging with respect to diversity of building appliances, targets and constraints to be optimized. Different B-DSM optimization models for the HEMS or BEMS have been established in the literature. For instance in [4], an appliance-scheduling is proposed taking into account a photovoltaic panel and a hybrid electric vehicle (PHEV). A dynamic multi-swarm with learning strategy is applied to solve the proposed model with the objective of reducing the weighted sum of the electricity payments, consumer's dissatisfaction and carbon dioxide emissions. In [5] the B-DSM with household appliances and a battery is proposed. The authors minimize the electricity cost and the discomfort level. The discomfort is modeled through the disparity between the baseline and the optimal schedule. The mixed integer non-linear optimization model is built and solved with AIMMS software. Authors in [6] proposed a framework fo...

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An optimal P2P energy trading model for smart homes in the smart grid

Cited by 112 publications

References 35 publications

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

Minimizing Energy Loss over Distance and Activating the Energy Trading System in Microgrid

A hybrid non-dominated sorting genetic algorithm for a multi-objective demand-side management problem in a smart building

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