Energy Optimization and Management of Demand Response Interactions in a Smart Campus

Barbato, Antimo; Bolchini, Cristiana; Geronazzo, Angela; Quintarelli, Elisa; Palamarciuc, Andrei; Piti, Alessandro; Rottondi, Cristina; Verticale, Giacomo

doi:10.3390/en9060398

Cited by 34 publications

(39 citation statements)

References 26 publications

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“…The schematic representation of such architecture is shown by the diagram of Figure 1. It is worth noting that an example of a real implementation of an EMS framework following this approach was proposed in [22].…”

Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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“…An activity-aware system to automate building systems in smart cities was developed in [27]. A framework to optimise energy management on smart campuses was proposed in [28]. A heating and cooling modelling system was proposed for minimising electricity consumption in smart cities [29].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Let ω iju be an auxiliary binary integer variable, which equals 1 if t ij − µ iu + µ ju = 0, and 0 otherwise. The complementary slackness condition, Equation (25), is replaced with the following set of constraints, Equations (26)- (28), resulting in the linearisation of KKT conditions:…”

Section: Linearisinng the Kkt Conditionsmentioning

confidence: 99%

Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

et al. 2019

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Many cities around the world are facing immense pressure due to the expediting growth rates in urban population levels. The notion of 'smart cities' has been proposed as a solution to enhance the sustainability of cities through effective urban management of governance, energy and transportation. The research presented herein examines the applicability of a mathematical framework to enhance the sustainability of decisions involved in zoning, land-use allocation and facility location within smart cities. In particular, a mathematical optimisation framework is proposed, which links through with other platforms in city settings, for optimising the zoning, land-use allocation, location of new buildings and the investment decisions made regarding infrastructure works in smart cities. Multiple objective functions are formulated to optimise social, economic and environmental considerations in the urban space. The impact on underlying traffic of location choices made for the newly introduced buildings is accounted for through optimised assignment of traffic to the underlying network. A case example on urban planning and infrastructure development within a smart city is used to demonstrate the applicability of the proposed method.Energies 2019, 12, 1318 2 of 23 increasingly reflected in the operations of existing cities, resulting in an interrelated platform between large numbers of citizens, transport networks, services and urban assets and facilities [9].Decision-making in planning and operations of smart cities needs to be structured around two main considerations, namely strategic and tactical decisions [10]. Yet, current emphasis in the literature is on the technical interfaces making up the various data-exchange enabling platforms placed within the cities, with little emphasis on the strategic and tactical urban planning aspects of smart cities. Within the area of strategic decision-making in city planning, zoning of the city and the location of its operating facilities, including schools, hospitals and so forth, are both of significant relevance [11]. An appropriate selection of zone clusters and the subsequent selection of locations to place buildings in form the main components that are involved in city design. In cities, the positioning of new buildings leads to the generation of traffic demand in the existing network structure [12]. This causes additional traffic loadings on the existing network, and if not well planned for, can result in major transportation delays to network users. Traffic congestion is thought to result in over $121 billion in losses [13] and can increase the amount of carbon emissions from traffic by more than 53% [14]. Another important strategic consideration is the development of the underlying travel network of the region. Specifically, the development of the transport network will be based on the capacities required to handle the initiated travel on the transport networks, while the operations of the network will in turn be associated with the established capacity of the network, along...

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“…In [17], the Lagrangian dual algorithm was employed to solve the nonconvex problem, and it came up with efficient demand response scheduling schemes. In [18], a complex telecommunication infrastructure was designed to manage the data exchange among the energy management system, generators, loads, and field sensors/actuators. In [19][20][21], the cost minimization of interactive consumers was studied based on the noncooperative game theory.…”

Section: Introductionmentioning

confidence: 99%

Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid

Bai

Yang

et al. 2017

Energies

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Demand-side energy management is used for regulating the consumers' energy usage in smart grid. With the guidance of the grid's price policy, the consumers can change their energy consumption in response. The objective of this study is jointly optimizing the load status and electric supply, in order to make a tradeoff between the electric cost and the thermal comfort. The problem is formulated into a nonconvex optimization model. The multiplier method is used to solve the constrained optimization, and the objective function is transformed to the augmented Lagrangian function without constraints. Hence, the Powell direction acceleration method with advance and retreat is applied to solve the unconstrained optimization. Numerical results show that the proposed algorithm can achieve the balance between the electric supply and demand, and the optimization variables converge to the optimum.

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Energy Optimization and Management of Demand Response Interactions in a Smart Campus

Cited by 34 publications

References 26 publications

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid

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