Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

Longe, Omowunmi Mary; Ouahada, Khmaies; Rimer, Suvendi; Harutyunyan, Ashot N.; Ferreira, H.C.

doi:10.3390/su9010120

Cited by 51 publications

(50 citation statements)

References 14 publications

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“…This stands in contrast to the results shown in [20], where a minimum is reached at medium range participation rate. In comparison to other studies, such as [8,5], we conclude that our dynamic game performs as good as their respective scheduling approach. At 100% participation rate, a reduction of −33.3% (5.8%) is achieved, in agreement with the results shown in Section 4.3.…”

Section: Influence Of Participation Rate and Forecasting Errorsmentioning

confidence: 40%

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Pilz

Al‐Fagih

2019

Dyn Games Appl

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Smart metering infrastructure allows for two-way communication and power transfer. Based on this promising technology, we propose a demand-side management (DSM) scheme for a residential neighbourhood of prosumers. Its core is a discrete time dynamic game to schedule individually owned home energy storage. The system model includes an advanced battery model, local generation of renewable energy, and forecasting errors for demand and generation.We derive a closed-form solution for the best-response problem of a player and construct an iterative algorithm to solve the game. Empirical analysis shows exponential convergence towards the Nash equilibrium. A comparison to a DSM scheme with a static game, reveals the advantages of the dynamic game approach. We provide an extensive analysis on the influence of the forecasting error on the outcome of the game. A key result demonstrates that our approach is robust even in the worst-case scenario. This grants considerable gains for the utility company organising the DSM scheme and its participants.

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Section: Influence Of Participation Rate and Forecasting Errorsmentioning

confidence: 40%

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Pilz

Al‐Fagih

2019

Dyn Games Appl

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“…Some literature has shown the contributions of DEG [6][7][8] and DES [7,[9][10][11][12][13] in the smart grid with possible locations in consumer premises [7,9,11,12] and centralised locations (owned by third parties) [10,13]. However, the former is easier and faster to deploy than the latter.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid

et al. 2017

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The contributions of Distributed Energy Generation (DEG) and Distributed Energy Storage (DES) for Demand Side Management (DSM) purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s). A grid-connected smart microgrid comprising heterogeneous (active and passive) smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA) installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction) and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR) demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.

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“…A smart meter within the smart grid provides detailed information on smart home power consumption [25]. It might be threatened by an attacker who decides to change the meter information [26].…”

Section: Threatening Of Energy Consumption Reportingmentioning

confidence: 99%

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

2017

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Abstract:The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead.

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Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

Cited by 51 publications

References 14 publications

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

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