A User-Centric View of a Demand Side Management Program: From Surveys to Simulation and Analysis

2022 ◽  
pp. 1-12
Author(s):  
Claudia De Vizia ◽  
Edoardo Patti ◽  
Enrico Macii ◽  
Lorenzo Bottaccioli
Keyword(s):  
Demand Side Management ◽  
Management Program ◽  
Demand Side ◽  
User Centric

Issues in forecasting demand-side management program impacts

1985 ◽  
Vol 73 (10) ◽  
pp. 1496-1502 ◽  
Author(s):  
B.A. Smith ◽  
M.R. McRae ◽  
E.L. Tabakin
Keyword(s):  
Demand Side Management ◽  
Management Program ◽  
Demand Side

A Scalable Energy–Economy Model for State-Level Policy Analysis Applied to a Demand-Side Management Program

2020 ◽  
Vol 34 (4) ◽  
pp. 372-386
Author(s):  
Zachary A. Wendling ◽  
David C. Warren ◽  
Barry M. Rubin ◽  
Sanya Carley ◽  
Kenneth R. Richards
Keyword(s):  
Green Building ◽  
State Level ◽  
Demand Side Management ◽  
Management Program ◽  
Demand Side ◽  
Low Carbon ◽  
Economic Sectors ◽  
Endogenous Variables ◽  
Performance Results ◽  
Low Carbon Energy

Over the past two decades, states and cities implemented low-carbon energy development, renewable portfolio standards, demand-side management (DSM), renewable energy production incentives, green building requirements, regional carbon trading agreements, and other energy-based economic development initiatives. Yet the dearth of state-level and substate-level models makes it difficult to predict the effects of such actions. This article addresses this shortcoming by presenting the performance results of the new Indiana Scalable Economy and Energy Model (IN-SEEM)—a model utilizing a dynamic, simultaneous equations framework—and demonstrates the model’s capabilities with an analysis of electricity price increases from a DSM program in the state of Indiana. Overall performance of the model is strong, with high adjusted R2 values and low mean absolute percent errors for most of 30 endogenous variables. A DSM price increase analysis finds variation in impact across the state’s 10 major economic sectors and small changes in energy consumption.

scholarly journals Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2047 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin ◽  
Chia-Ching Kung ◽  
Ming-Han Chung ◽  
I-Hsuan Yen
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Modern Society ◽  
Smart Homes ◽  
Demand Side Management ◽  
Demand Side ◽  
Smart Meters ◽  
Smart Power ◽  
Iot Applications ◽  
User Centric

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

scholarly journals Multiobjective Optimization for Demand Side Management Program in Smart Grid

2018 ◽  
Vol 14 (4) ◽  
pp. 1482-1490 ◽  
Author(s):  
Dan Li ◽  
Wei-Yu Chiu ◽  
Hongjian Sun ◽  
H. Vincent Poor
Keyword(s):  
Multiobjective Optimization ◽  
Smart Grid ◽  
Demand Side Management ◽  
Management Program ◽  
Demand Side

scholarly journals Impact of Demand-Side Management on the Reliability of Generation Systems

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2155 ◽  
Author(s):  
Hussein Jumma Jabir ◽  
Jiashen Teh ◽  
Dahaman Ishak ◽  
Hamza Abunima
Keyword(s):  
Power Supply ◽  
Sequential Monte Carlo ◽  
Demand Side Management ◽  
Management Program ◽  
Electrical Network ◽  
Demand Side ◽  
Peak Period ◽  
Load Shifting ◽  
Shifting Strategy ◽  
Unused Capacity

The load shifting strategy is a form of demand side management program suitable for increasing the reliability of power supply in an electrical network. It functions by clipping the load demand that is above an operator-defined level, at which time is known as peak period, and replaces it at off-peak periods. The load shifting strategy is conventionally performed using the preventive load shifting (PLS) program. In this paper, the corrective load shifting (CLS) program is proven as the better alternative. PLS is implemented when power systems experience contingencies that jeopardise the reliability of the power supply, whereas CLS is implemented only when the inadequacy of the power supply is encountered. The disadvantages of the PLS approach are twofold. First, the clipped energy cannot be totally recovered when it is more than the unused capacity of the off-peak period. The unused capacity is the maximum amount of extra load that can be filled before exceeding the operator-defined level. Second, the PLS approach performs load curtailment without discrimination. This means that load clipping is performed as long as the load is above the operator-defined level even if the power supply is adequate. The CLS program has none of these disadvantages because it is implemented only when there is power supply inadequacy, during which the amount of load clipping is mostly much smaller than the unused capacity of the off-peak period. The performance of the CLS was compared with the PLS by considering chronological load model, duty cycle and the probability of start-up failure for peaking and cycling generators, planned maintenance of the generators and load forecast uncertainty. A newly proposed expected-energy-not-recovered (EENR) index and the well-known expected-energy-not-supplied (EENS) were used to evaluate the performance of proposed CLS. Due to the chronological factor and huge combinations of power system states, the sequential Monte Carlo was employed in this study. The results from this paper show that the proposed CLS yields lower EENS and EENR than PLS and is, therefore, a more robust strategy to be implemented.

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