Smart Lighting Systems as a Demand Response Solution for Future Smart Grids

Lee, Chi Kwan; Liu, Heng; Fuhs, Desiree; Kores, Andre; Waffenschmidt, Eberhard

doi:10.1109/jestpe.2018.2890385

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…where •, • represents the inner product on H, and h(x) := 1/(1 + e −x ) ensures that each action is always within [0, 1]. Therefore, the optimal policy for room i can be characterized by solving (17) with respect to θ i as…”

Section: B Learning-based Solutionmentioning

confidence: 99%

“…There are many types in commercial buildings such as offices, schools, hospitals, and so on, and the major energy consumption appliances can be different highly depending on the type of building. In this work, we consider the building, where the energy consumption of the lighting and air conditioning (AC) systems constitutes a dominant portion of the total building energy usage [17], [18]. A key challenge is that one must cope with the stochasticity of various environmental variables such as the ambient sunlight and temperature.…”

Section: Introductionmentioning

confidence: 99%

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Building Energy Management for Demand Response Using Kernel Lifelong Learning

et al. 2020

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Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy management is a critical component for the DR portfolio. In this study, DR control policies for lighting and air-conditioner systems for the individual spaces in buildings are proposed. The policies are designed to achieve the energy reduction amount specified in the DR request while minimizing the user discomfort. A significant challenge is to cope with the uncertainty of various environmental factors such as the solar illuminance and ambient temperature, as well as the psycho-economic factors such as the energy usage preferences of the occupants. We employ a datadriven machine learning approach to tackle this challenge. Our novel idea is to take advantage of the structural similarity of the control policies across the spaces in a lifelong multi-task learning framework. To accommodate significant nonlinearity in efficient policies, a kernel-based learning approach is pursued. The dual decomposition method is employed to relax the constraint coupled across the spaces, which allows solving the overall learning problem via a series of unconstrained subproblems. The efficacy of the proposed method is verified by numerical experiments based on semi-real data sets.

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Section: B Learning-based Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Energy Management for Demand Response Using Kernel Lifelong Learning

et al. 2020

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“…In recent years, the topic of urban smart lighting gained attention and several aspects of this technology were investigated separately. Some papers deal with the design of the hardware (e.g., light controller, lamp driver) [37][38][39][40][41], whereas other papers deal with the automatic, context aware, control of illumination [29,42,43]. There are papers concerning software/cloud-computing issues in smart city scenarios [44][45][46], others focusing on energy efficiency [47][48][49][50][51][52], as well as paper presenting smart lighting solutions based on specific communication technologies [53][54][55][56][57].…”

Section: Related Workmentioning

confidence: 99%

Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems

Pasolini

Toppan²,

Zabini

et al. 2019

Applied Sciences

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Street lighting characterizes many smart city initiatives around the world. In fact, significant savings can be achieved by not only replacing traditional luminaires with low-power LEDs, but also providing streetlights with smart light controllers and network connectivity, allowing the introduction of a sensible light intensity management and reduction of maintenance costs. Moreover, if designed with a far-looking view, smart lighting infrastructure could also support city-wide Internet of Things services, becoming key enablers of the smart city revolution, also in the 5G perspective. In this paper, we provide a thorough discussion on network architectures and communication technologies that could be adopted for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation and in-field testing, we also outline the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology considered. Finally, we introduce some additional services that a smart public lighting infrastructure could support and discuss the benefits that would arise from integration with the upcoming 5G cellular network.In this framework, the momentum for smart public lighting is mounting, as several trials and large-scale implementations showed their effectiveness [5]. In particular, LED fixtures are expected to dramatically reduce urban electricity costs, now due to street lighting for a share of 40% [6]. Smart luminaires will dynamically adapt lighting to actual needs, for instance lowering the lumen output when there are low traffic volumes and good visibility conditions are detected.Important references in this regard are the "GPP Revision of the EU Green Public Procurement Criteria for Road Lighting and traffic signals" [7], authored by the Joint Research Centre (JRC) of the European Commission, and the CEN/TR 13201 lighting standards [8][9][10][11][12], which define how to properly design lighting systems. As reported in Reference [7], "due to the multiple benefits of dimming, dimming controls must be installed in all cases unless, in exceptional circumstances, it can be demonstrated that the total cost of ownership would increase by installing dimming controls. The EN 13201 standard itself [8][9][10][11][12] recognizes that the required lighting levels are dynamic in nature and an appropriate lighting level at all times can only be ensured with adequate dimming control during off-peak hours. Dimming has obvious environmental benefits via lower energy consumption and reduced light pollution. Furthermore, dimming can enhance the lifetime of LED luminaires due to a reduced risk of overheating, which is the principal cause of abrupt LED failure." In this regard, results from a global trial of LED streetlights show that, when coupled with smart controls, LED fixtures can deliver electricity savings of up to 80% over classic lighting technologies [13].Beyond energy savings, since each smart streetlight n...

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“…Lighting loads are one of the major loads in the domestic load profile [19], and LED lighting loads are replacing the other types of lighting loads due to their benefits [20], [21]. In [22], it has been stated that smart lighting systems such as driver-controlled LEDs are the DR solutions for smart grids. In [23], a decentralized control strategy has been presented for a two-area interconnected power system to facilitate the active participation of LEDLLs; Each LED load has responded independently to the local frequency signal.…”

Section: Introductionmentioning

confidence: 99%

A Novel and Optimal Battery Sizing Procedure Based on MG Frequency Security Criterion Using Coordinated Application of BESS, LED Lighting Loads, and Photovoltaic Systems

2020

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This paper proposes a novel and optimal battery sizing procedure for the primary frequency control (PFC) of islanded microgrid (MG). The Battery Energy Storage System (BESS), Photovoltaic (PV) systems and LED Lighting Loads (LEDLLs) are coordinated to quickly intercept frequency deviation in the stage of PFC. The PVs decrease their power generation in the case of surplus of power generation. The LEDLLs decrease their power consumption in the case of power shortage. The BESS participates in PFC in both cases by injecting/absorbing power. Some batteries with overloading characteristics are capable of fast discharge/charge for a short period, which can be used to reduce the required battery size for PFC application. The conventional overloading characteristics is based on constant power discharge/charge, but the BESS power varies in response to frequency deviation. To overcome this problem, a modified overloading characteristic is presented based on variable power discharge/charge, which is used to propose a battery sizing algorithm. The Genetic Algorithm is used to optimally determine the frequency controllers' coefficients of the BESS, PVs and LEDLLs to minimize the required battery size while maintaining the MG frequency within safe operational limits. The proposed battery sizing procedure is evaluated on the CIGRE low voltage benchmark system using simulation in MATLAB/Simulink software. The results show that beside the overloading characteristics of the battery, the participation of PVs and LEDLLs in PFC also reduces the required battery size, because their participation reduces the share of BESS participation in PFC.

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Smart Lighting Systems as a Demand Response Solution for Future Smart Grids

Cited by 16 publications

References 19 publications

Building Energy Management for Demand Response Using Kernel Lifelong Learning

Building Energy Management for Demand Response Using Kernel Lifelong Learning

Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems

A Novel and Optimal Battery Sizing Procedure Based on MG Frequency Security Criterion Using Coordinated Application of BESS, LED Lighting Loads, and Photovoltaic Systems

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