Long Term Options for Energy Supply and Demand Side Management

1994 ◽  
pp. 77-90
Author(s):  
Tom Morron ◽  
Fred Denny
Keyword(s):  
Energy Supply ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Demand Side

Integrating supply and demand-side management in renewable-based energy systems

Energy ◽  
2021 ◽  
pp. 120978
Author(s):  
Géremi Gilson Dranka ◽  
Paula Ferreira ◽  
A. Ismael F. Vaz
Keyword(s):  
Supply And Demand ◽  
Energy Systems ◽  
Demand Side Management ◽  
Demand Side

Production Process Modeling for Demand Side Management

2017 ◽  
Vol 871 ◽  
pp. 77-86
Author(s):  
Stefanie Kabelitz ◽  
Sergii Kolomiichuk
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Business Practices ◽  
Demand Side ◽  
Manufacturing Companies ◽  
Consumption Data

The supply of electricity is growing increasingly dependent on the weather as the share of renewable energies increases. Different measures can nevertheless maintain grid reliability and quality. These include the use of storage technologies, upgrades of the grid and options for responsiveness to supply and demand. This paper focuses on demand side management and the use of flexibility in production processes. First, the framework of Germany’s energy policy is presented and direct and indirect incentives for businesses to seek as well as to provide flexibility capabilities are highlighted. Converting this framework into a mixed integer program leads to multi-objective optimization. The challenge inherent to this method is realistically mapping the different objectives that affect business practices directly and indirectly in a variety of laws. An example is introduced to demonstrate the complexity of the model and examine the energy flexibility. Second, manufacturing companies’ energy efficiency is assessed under the frequently occurring conditions of heavily aggregated energy consumption data and of information with insufficient depth of detail to perform certain analyses, formulate actions or optimize processes. The findings obtained from the energy assessment and energy consumption projections are used to model the production system’s energy efficiency and thus facilitate optimization. Methods of data mining and machine learning are employed to project energy consumption. Aggregated energy consumption data and different production and environmental parameters are used to assess indirectly measured consumers and link projections of energy consumption with the production schedule.

Smart Factory Meets Smart Grid: Cyber-Physical Compressed Air Systems Enable Demand Side Management in Industrial Environments

2015 ◽  
Vol 805 ◽  
pp. 25-31 ◽  
Author(s):  
Ralf Boehm ◽  
Johannes Bürner ◽  
Jörg Franke
Keyword(s):  
Business Models ◽  
Service Providers ◽  
Supply And Demand ◽  
Electric Energy ◽  
Demand Side Management ◽  
Compressed Air ◽  
Demand Side ◽  
Industrial Enterprises ◽  
Industrial Environments ◽  
Air Compressors

In electric energy systems based on renewable generation plants supply and demand often do not occur in the same period of time. Consequently demand side management is gaining importance whereby decentralized automation offers opportunities in industrial environments. Compressed air systems on industrial plants consist of air compressors, compressed air reservoirs and compressed air lines. With suitable dimensioning those industrial compressed-air systems can be used for demand side management purpose. As power consumption of industrial air compressors ranges between a few and several hundred kilowatts each, swarms of communicatively connected air compressors can contribute to the stabilization of power grids. To avoid costly production downtime it is to ensure, that a reliable, non-disruptive supply of compressed air can be maintained at all time. Industrial compressed air systems equipped with automation technology and artificial intelligence, which hereinafter are referred to as Cyber-Physical Compressed Air Systems (CPCAS), allow new business models for utilities, industrial enterprises, compressor manufacturers and service providers. In addition to basic operating parameters like current air pressure and status, those systems can process further information and create, for example, profiles on compressed air consumption over time. By enriching those profiles with data on pressure, volumes, system restrictions and current production requirements (plans), the CPCAS can identify the available potential for demand side management. Ipso facto predictive power on electricity consumption is increasing. By providing the information obtained to the power company or a service provider, savings in electricity costs may be achieved. Expenses within the industrial company may be lowered further as compliance with agreed load limits is being improved by automatic shutdown of air compressors upon reaching the load limit. Within this article the structure of the aforementioned Cyber-Physical Compressed Air Systems is presented in more detail, relations between the major actors are being shown and possible business models are being introduced.

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

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.

LONG-TERM ENERGY SUPPLY AND DEMAND PROJECTIONS

OPEC Review ◽  
1977 ◽  
Vol 1 (6) ◽  
pp. 7-21
Author(s):  
Amir H. Maghen ◽  
Ivan Bejarano G.
Keyword(s):  
Energy Supply ◽  
Supply And Demand ◽  
Term Energy

Study on Demand Side Management of Railway Electricity System

2014 ◽  
Vol 631-632 ◽  
pp. 1347-1350
Author(s):  
Bing Feng Liu
Keyword(s):  
Power System ◽  
Index System ◽  
Demand Side Management ◽  
Fuzzy Evaluation ◽  
Demand Side ◽  
Analytic Hierarchy ◽  
Electricity System ◽  
The Index System ◽  
Hierarchy Process

An index system for evaluating the demand side management (DSM) on railway temporary power system is established in this thesis, and the part weight and the whole weight of the index system are calculated by the analytic hierarchy process (AHP). The basic theory of fuzzy evaluation is introduced, and the feasibility of applying it to DSM on railway temporary power system is analyzed. Through the establishment of index system, evaluation and comparison to DSM projects can be carried out horizontal and vertical. Then identify gaps and weaknesses, analyze the causes, guide the consciousness of energy conservation and environmental protection, supervised of electricity in an orderly manner, establish DSM long-term management mechanism.

The long-term forecast of Taiwan’s energy supply and demand: LEAP model application

Energy Policy ◽  
2011 ◽  
Vol 39 (11) ◽  
pp. 6790-6803 ◽  
Author(s):  
Yophy Huang ◽  
Yunchang Jeffrey Bor ◽  
Chieh-Yu Peng
Keyword(s):  
Energy Supply ◽  
Supply And Demand ◽  
Model Application ◽  
Term Forecast

Study on DSM Energy-Saving Effect Evaluation under the Smart Grid Environment

2012 ◽  
Vol 209-211 ◽  
pp. 1867-1870 ◽  
Author(s):  
Hao Chi Guo ◽  
Jin Peng Liu ◽  
Huan Huan Qiao ◽  
Guan Qing Wang
Keyword(s):  
Smart Grid ◽  
Evaluation System ◽  
Supply And Demand ◽  
Demand Side Management ◽  
Demand Side ◽  
Management Effectiveness ◽  
Grid Environment ◽  
The Index System ◽  
Implementation Effect ◽  
Saving Effect

China's power conflicts between supply and demand have become increasingly prominent. The smart grid has become an inevitable trend of network development at the same time, and DSM implementation effect in smart grid research increasingly attracts people’s attention. Through the analysis of different subjects in demand side management, the paper established demand side management effectiveness evaluation system in smart grid from the aspects of economic , environmental and social. Finally, empirical research verified the validity of the index system ,which provides a good reference for improving the level of demand side management.

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