Demand-side Management Programs – A joint environmental protection action. Case study: The lighting system in the Campus of the University POLITEHNICA of Bucharest

The current development of Smart Grids, combined with the pressure enforced by national legislation as a direct effect of the 2012/27/EU and the 2018/844/EU Directives and the ever-growing energy demand lead to a new set of challenges for both the end-users and the utility companies, under the form of optimizing the EPIs (Energy Performance Indicators), reducing the Environmental Impact and flattening the Power Demand Curve. In this paper, the economical viability and the potential environmental impact reduction obtained by implementing a DSM (Demand–Side Management) program in the Campus of the University POLITEHNICA of Bucharest were analysed. The energy boundary consisted of all the 26 Student Dorms and the main Significant Energy Use) consisted of the lighting system. Four different scenarios were conceived, taking into account the initial investment and the energy savings sharing between the end-user and the Utility Company. Based on these scenarios, a technical-economic model is presented. Using the data gathered on-site and the DSM program mode, relevant results were obtained and a development solution for such projects was proposed. In the final part of the paper, the predicted Environmental Impact Reduction was quantified and analysed, under the form of the Carbon Footprint generated, respectively avoided by implementing the most economically efficient DSM program development solution.

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Energy Demand Side Management in the Lack of Smart Grids

Sustainable Future Energy Technology and Supply Chains - Green Energy and Technology ◽

10.1007/978-3-319-02696-1_7 ◽

2015 ◽

pp. 157-170 ◽

Cited By ~ 1

Author(s):

Zeynep Bektas ◽

M. Ozgur Kayalica

Keyword(s):

Smart Grids ◽

Energy Demand ◽

Demand Side Management ◽

Demand Side

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Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids

IEEE Internet of Things Journal ◽

10.1109/jiot.2016.2556006 ◽

2017 ◽

Vol 4 (2) ◽

pp. 374-383 ◽

Cited By ~ 52

Author(s):

Te-Chuan Chiu ◽

Yuan-Yao Shih ◽

Ai-Chun Pang ◽

Che-Wei Pai

Keyword(s):

Renewable Energy ◽

Smart Grids ◽

Energy Demand ◽

Demand Side Management ◽

Demand Side

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A Hybrid Technique for Residential Load Scheduling in Smart Grids Demand Side Management

Advances in Internet, Data & Web Technologies - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-319-75928-9_93 ◽

2018 ◽

pp. 1007-1017

Author(s):

Muhammad Hassan Rahim ◽

Adia Khalid ◽

Ayesha Zafar ◽

Fozia Feroze ◽

Sahar Rahim ◽

...

Keyword(s):

Smart Grids ◽

Demand Side Management ◽

Hybrid Technique ◽

Demand Side ◽

Load Scheduling

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DEMAND-SIDE MANAGEMENT TO IMPROVE POWER SYSTEM PERFORMANCE WITHIN SMART GRIDS ENVIRONMENT

ERJ. Engineering Research Journal ◽

10.21608/erjm.2014.66939 ◽

2014 ◽

Vol 37 (4) ◽

pp. 371-377

Author(s):

A. Barakat ◽

A. Abaza ◽

Ahmed M. Azmy

Keyword(s):

Power System ◽

System Performance ◽

Smart Grids ◽

Demand Side Management ◽

Demand Side ◽

Power System Performance

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Distributed Demand Side Management with Stochastic Wind Power Forecasting

10.36227/techrxiv.13360196 ◽

2020 ◽

Author(s):

Paolo Scarabaggio ◽

Sergio Grammatico ◽

Raffaele Carli ◽

Mariagrazia Dotoli

Keyword(s):

Wind Speed ◽

Wind Power ◽

Smart Grids ◽

Demand Side Management ◽

Expected Value ◽

Noncooperative Game ◽

Theory Approach ◽

Demand Side ◽

Wind Power Forecasting ◽

Power Forecasting

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

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Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2021-0104 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Souhil Mouassa ◽

Marcos Tostado-Véliz ◽

Francisco Jurado

Keyword(s):

Energy Demand ◽

Supply And Demand ◽

Optimization Technique ◽

Smart Homes ◽

Demand Side Management ◽

Time Interval ◽

Demand Side ◽

Artificial Ecosystem ◽

Pricing Schemes ◽

Operational Time

Abstract With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

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