Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

Rashid, Mamun Ur; Alotaibi, Majed A.; Chowdhury, A. Hasib; Rahman, M. Sohel; Alam, Md. Shafiul; Ma, Hossain; Abido, M. A.

doi:10.3390/en14041060

Cited by 25 publications

(13 citation statements)

References 37 publications

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“…SoC(t)—State of charge of battery (%) 26

italicSoC ((), t) goodbreak= \frac{italicEB ((), t)}{{italicEB}_{italicnom}} goodbreak\times 100

…”

Section: Proposed Methodsmentioning

confidence: 99%

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Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Gounder¹,

Subramanian²

2022

Intl J of Energy Research

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Summary Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible generation‐demand model. An optimization algorithm using mixed integer linear programming (MILP) has been formulated to minimize the operating cost and emission of dispatchable power generation, with the help of demand response. Usually, in renewable energy–based grid‐connected microgrid, the batteries are managed under partial state of charge (SoC) conditions due to the limit of power imported from grid. The proposed MILP model ensures full SoC operation and safe charging or discharging dynamics of the battery in order to enhance its lifespan. Moreover, the day‐ahead scheduling of household appliances is carried out using a novel hybrid knapsack method, which combines binary and fractional knapsack algorithms. An electric vehicle battery is considered as a flexible power load, which offers an unique way of approach in scheduling of appliances. The results confirm that the power demanded by the appliances is fulfilled at the user‐specified hour for maximum comfort along with minimum operating cost of microgrid. Generic algebraic modeling system (GAMS) tool is used to run the proposed algorithms.

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“…SoC(t)—State of charge of battery (%) 26

italicSoC ((), t) goodbreak= \frac{italicEB ((), t)}{{italicEB}_{italicnom}} goodbreak\times 100

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…SoC(t)-State of charge of battery (%) 26 Equation (3) determines SoC of battery at each time interval, and Equation (4) determines the battery energy level at each time using charging and discharging efficiencies.…”

Section: Battery Parametersmentioning

confidence: 99%

Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Gounder¹,

Subramanian²

2022

Intl J of Energy Research

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“…It stores the excess energy in a low demand period and releases this stored energy during high demand or low generation period. The operational cost of the battery is given as [24]: The battery capacity is taken as 9500 kWh and with a lifetime of 5 years. The round trip efficiency is 90%.…”

Section: Batterymentioning

confidence: 99%

Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector

Sharma¹,

Mishra

Dhillon

et al. 2022

Energies

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The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.

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“…Dynamic pricing is one of demand-side management methods that can reduce the on-peak demand by charging different prices at different times according to demand. TOU based demand-side management is proposed in [3]. Furthermore, dynamic pricing can help utilities reduce investment cost of a power system by shifting peak loads from peak to off-peak hours.…”

Section: Introductionmentioning

confidence: 99%

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Jang¹,

Jeong²,

Kim³

et al. 2021

Energies

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Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

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Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

Cited by 25 publications

References 37 publications

Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Feasibility of Solar Grid-Based Industrial Virtual Power Plant for Optimal Energy Scheduling: A Case of Indian Power Sector

Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

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