A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation

Li, Meng; Quilumba, Franklin L.; Lee, Wei-Jen

doi:10.1109/tia.2015.2427286

Cited by 45 publications

(16 citation statements)

References 13 publications

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“…In [18], the current phase of Paris agreement has focused on developing a global approach, which limits the GHG emissions of all countries. In recent years, authors have presented various models and methods for the optimal operation of such systems [2][3][4][5][6][7][8]. The objectives for optimal HEMS operation include minimizing CE, TBD, peak-to-average ratio (PAR) and peak/permanent demands [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

“…Most of the models for DRSREOD-based HEMS presented in the recent past are based on optimal scheduling of SHAs integrated with the optimal dispatch of RESs and ESSs. HEMS problems for these models have been solved to reduce CE and discomfort for the consumer, and to minimize peak load/PAR and cost of generation for the utility [4][5][6][7][8]. Recently, in the context of worldwide concerns over GHG emissions, authors have focused on the reduction in emission as an objective for DRSREOD-based HEMS.…”

Section: Emissions Reduction Using Drsreod-based Hemssmentioning

confidence: 99%

“…The DRSREOD-based HEMS operation schedules the load in coordination with the optimal dispatch of the power grid, renewable energy sources (RESs) and energy storage systems (ESSs). The operation of such HEMS introduces additional benefits by minimizing the electricity cost, minimizing high demands and permanent demands, increasing total cost minimization and empowering the selling of the extra power to the utility [4][5][6][7][8][9][10][11]. The aforementioned HEMSs are modeled to optimize the objectives comprising the net CE (CEnet), consumer discomfort/inconvenience, and peak and permanent demands.…”

Section: Introductionmentioning

confidence: 99%

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An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

et al. 2018

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A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.

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Section: Related Workmentioning

confidence: 99%

Section: Emissions Reduction Using Drsreod-based Hemssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

et al. 2018

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“…However, this ignores the potential value of residential and agricultural users. These small consumers are numerous [4], especially in remote rural areas. There are more than one billion people worldwide living in rural areas, and some are facing severe energy shortages [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Optimized Energy Scheduling of Rural Microgrid

et al. 2019

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This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.

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“…Therefore, the problem of renewable energy consumption can be solved if the demand-side loads consume renewable energy actively. On the one hand, air conditioning loads, electric water heaters, and other thermostatically-controlled loads have great potential for scheduling [1][2][3][4], and they can temporarily store electrical energy in the form of thermal energy [5][6][7][8][9][10]. On the other hand, for the users, fine-tuning the working time and temperature, the thermal control loads have little impact on user comfort; on the power supply side, they can quickly respond to power dispatching [11].…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

Liang

Han

2018

Applied Sciences

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With poor peak load regulating capacity, renewable energy generation is intermittent and fluctuating, which results in the insufficient acceptance capacity of the power grid. Based on the state-queuing model of aggregate air conditioning loads, this paper develops a control algorithm to achieve renewable energy consumption and output tracking. The load curves of the aggregate air conditioning loads can be controlled by changing the initial temperature distribution. Under different temperature distributions, the load curves represent a fixed fluctuation, which is the basis of output tracking. A virtual load curve set is established based on the state-queuing model. Regarding the load curves as basic signals, the expected renewable energy output can be tracked via an optimal combination of the basic load curves. The validity of the algorithm is testified by numerical emulation data.

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A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation

Cited by 45 publications

References 13 publications

An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

Research on Optimized Energy Scheduling of Rural Microgrid

Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

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