Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management

Javaid, Nadeem; Hafeez, Ghulam; Iqbal, Sohail; Alrajeh, Nabil; Alabed, Mohamad Souheil; Guizani, Mohsen

doi:10.1109/access.2018.2866461

Cited by 148 publications

(79 citation statements)

References 28 publications

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“…As for the harbour master's office, it is open from 7am to 16pm on working days during winter and every day during the other seasons. Office occupants use heating mode of operation of the HVAC system only, imposing the thermal comfort in the range [19][20][21][22] °C. The settling time of the office is estimated at 1.5 hour, whilst the electric consumption of heat pump for one hour is estimated at 9 kWh/h.…”

Section: Scheduling Methodsmentioning

confidence: 99%

“…For example, an offline building energy management system is outlined in [18] to manage selected household loads according to customer preferences and the demand limit, aiming at reducing the demand and energy consumption costs. A similar smart energy management system is proposed in [19], where evolutionary optimization algorithms are proposed to schedule appliances of interconnected multiple homes. In [20] a day-ahead scheduling algorithm including power forecasting module, a BESS management module and an optimization module is implemented to minimize the total operational cost of an energy district.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

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This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under timevarying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 hour resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach.

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Section: Scheduling Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

View full text Add to dashboard Cite

show abstract

“…They scheduled different office appliances to reduce the cost and PAR. In [19], renewable energy sources (RESs) are integrated into the SG system to make the utility more stable, reduce electricity cost and reduce waiting time. SG provides the consumer the opportunity to integrate RESs to play significant role DSM.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities

et al. 2020

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Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.

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“…The end users in SG are educated to optimize their load consumption; however, for the last few years, insightful researchers have proposed autonomous DSM programs [26]. The purpose of the DSM is to autonomously optimize the load consumption on the demand side.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

Self Cite

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The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud-fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

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Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management

Cited by 148 publications

References 28 publications

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

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