Energy Cost Optimization and DER Scheduling for Unified Energy Management System of Residential Neighborhood

Nizami, Mohammad Sohrab Hasan; Hossain, M. J.; Mahmud, Khizir; Ravishankar, Jayashri

doi:10.1109/eeeic.2018.8493732

Cited by 19 publications

(14 citation statements)

References 15 publications

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“…However, these studies did not account for charging and discharging of EVs while also ignoring the limitations of distributed generation capacity installation in high density residential buildings. Some of the latter works include extensions to an aggregated EMS, e.g., for a neighbourhood [15], whilst others focus solely on the design and performance of aggregated EMSs for an office building [16], [17] or apartment block [4]. These EMS are also usually optimized with respect to a specific metric of interest, e.g., to minimize aggregated grid impacts and/or energy costs [10], but few consider the scenario-based travel and charging patterns.…”

Section: A Literature Reviewmentioning

confidence: 99%

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Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources

et al. 2021

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This paper proposes two electric energy management systems (EMSs) in the context of a gridconnected residential neighbourhood with electric vehicles (EVs), battery storage, and solar photovoltaic (PV) generation. The EMSs were developed to minimize the cost of electricity whilst having no impact on routine individual energy needs and travel patterns. The EMSs were evaluated using common sets of real data with the aim to compare the effectiveness of a centralized EMS with decentralized EMS. The models also accounted for the battery capacity degradation and the associated costs. Simulation studies and numerical analyses were presented to validate the effectiveness of the proposed EMSs considering a high-density residential building in Sydney, Australia. The simulation results indicate that the centralized EMS is more effective compared to the decentralized EMS in terms of cost savings. It is also observed that the energy management strategies significantly reduce the energy drawn from the grid compared to un-optimized energy management schemes.

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Section: A Literature Reviewmentioning

confidence: 99%

“…Therefore, the objective function for both the strategies is same and expressed mathematically as eq. (15).…”

Section: ) Objective Functionmentioning

confidence: 99%

Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources

et al. 2021

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“…The aim of the proposed coordination method is to reduce the electricity costs of households by increasing the usage of renewable energy in the neighborhood and enabling electricity trade among smart homes. Nizami et al [35] introduced an integrated approach to the HEMS for a residential area that reduces the demand peaks in the neighborhood and electricity bill of participating users. Joo and Choi [36] proposed a two-level-distributed HEMS algorithm.…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Energy Management of Multi-Smart Homes With Distributed Generation and Storage

2019

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This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.

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“…This involves implementing an energy management algorithm, which considers the behaviour of the residential household load, as seen from the gridproducing/selling energy when prices are high, and consuming/buying energy when prices are low. The general idea has been investigated in the literature, in relation to other countries and cases [22][23][24][25][26][27]. This paper investigates BTMS with unchanged energy consumption behaviour in Denmark for residential consumers.…”

Section: The Flexible Pricing Schemementioning

confidence: 99%

Case Study of Residential PV Power and Battery Storage with the Danish Flexible Pricing Scheme

2019

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The economic viability of renewable energy generation is vital for sustainability. Ensuring that optimal operation is always achieved, using energy management systems and control algorithms, is essential in this endeavor. Here, a new real-time pricing scheme, the Danish flexible pricing scheme, illustrates how residential PV and battery systems can optimize the electricity bill of households, without changing consumption behavior or providing grid services in exchange. This means that the only addition is PV production, storage, and control. A case study is constructed from Danish household consumption data, irradiance measurements, and recorded spot prices. With the input data, the pricing scheme, and the energy flow, simulation models are computed in MATLAB, thereby validating the algorithmic potential and finding the best strategy for charging and discharging the energy storage unit. Different methods are compared to list the viable options and evaluate them, based on the economic feasibility for the household. Furthermore, a discussion of the system implementation is also included to highlight technical difficulties, co-integration opportunities, short-comings, and advantages present in the case study. In conclusion, it is possible to make renewable energy generation, and storage, viable for a Danish residential household under the new pricing scheme.

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Energy Cost Optimization and DER Scheduling for Unified Energy Management System of Residential Neighborhood

Cited by 19 publications

References 15 publications

Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources

Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources

Two-Stage Energy Management of Multi-Smart Homes With Distributed Generation and Storage

Case Study of Residential PV Power and Battery Storage with the Danish Flexible Pricing Scheme

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