Electrical Energy Management in the Cement Industry

1979 ◽  
Vol IA-15 (4) ◽  
pp. 341-347 ◽  
Author(s):  
George E. MacDonald ◽  
Anthony C. Lordi ◽  
John J. Kovach
Keyword(s):  
Energy Management ◽  
Electrical Energy ◽  
Cement Industry

scholarly journals Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 537
Author(s):  
Rittichai Liemthong ◽  
Chitchai Srithapon ◽  
Prasanta K. Ghosh ◽  
Rongrit Chatthaworn
Keyword(s):  
Energy Management ◽  
Energy Cost ◽  
Management Strategy ◽  
Cost Minimization ◽  
Distribution Network ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Heuristic Optimization ◽  
Energy Management Strategy ◽  
Home Energy Management

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

scholarly journals A review of electrical energy management techniques: supply and consumer side (industries)

2009 ◽  
Vol 20 (3) ◽  
pp. 14-21 ◽  
Author(s):  
Afua Mohamed ◽  
Mohamed Tariq Khan
Keyword(s):  
Energy Management ◽  
Electrical Energy ◽  
Load Control ◽  
Supply Side ◽  
Demand Side ◽  
Interruptible Load ◽  
Direct Load Control ◽  
Management Techniques ◽  
Power Utilities ◽  
Demand Control

A review of electrical energy management tech-niques on the supply side and demand side is pre-sented. The paper suggests that direct load control, interruptible load control, and time of use (TOU) are the main load management techniques used on the supply side (SS). The supply side authorities normally design these techniques and implement them on demand side consumers. Load manage-ment (LM) initiated on the demand side leads to valley filling and peak clipping. Power factor correc-tion (PFC) techniques have also been analysed and presented. It has been observed that many power utilities, especially in developing countries, have neither developed nor implemented DSM for their electrical energy management. This paper proposes that the existing PFC techniques should be re-eval-uated especially when loads are nonlinear. It also recommends automatic demand control methods to be used on the demand side in order to acquire optimal energy consumption. This would lead to improved reliability of the supply side and thereby reducing environmental degradation.

Electrical energy management for low-voltage clients

2007 ◽  
Author(s):  
B. Huyck ◽  
J Cappelle ◽  
K. Stul ◽  
K. Duerloo ◽  
J. Debaenst ◽  
...  
Keyword(s):  
Energy Management ◽  
Low Voltage ◽  
Electrical Energy

Demand-Side Response Smart Grid Technique for Optimized Energy Use

2011 ◽  
pp. 137-163 ◽  
Author(s):  
Fouad Kamel ◽  
Marwan Marwan
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Electricity Market ◽  
Energy Use ◽  
Combustion Engine ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
Electrical Energy ◽  
Electricity System ◽  
Time Price

The chapter describes a dynamic smart grid concept that enables electricity end-users to be acting on controlling, shifting, or curtailing own demand to avoid peak-demand conditions according to information received about electricity market conditions over the Internet. Computer-controlled switches are used to give users the ability to control and curtail demand on a user’s premises as necessary, following a preset user’s preferences. The computerized switching provides the ability to accommodate local renewable energy sources as available. The concept offers further the ability to integrate charging electrical vehicles during off-peak periods, helping thus substantially improving the utilization of the whole electricity system. The approach is pursuing improved use of electrical energy associated with improved energy management, reduced electricity prices and reduced pollution caused by excessive use of combustion engine in transport. The technique is inherently restricted to take effect in frame of energy tariff regimes based on real-time price made to encourage and reward conscious users being proactively participating in holistic energy management strategies.

Rule-Based Alternator Control Using Predicted Velocity for Energy Management Strategy

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Jaewook Shin ◽  
Haksu Kim ◽  
Seungeon Baek ◽  
Myoungho Sunwoo ◽  
Manbae Han
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Algorithm ◽  
Internal Combustion Engines ◽  
Electrical Energy ◽  
Control Mode ◽  
Pi Control ◽  
Electrical System ◽  
Energy Management Strategy ◽  
Rule Based

AbstractThe market concern of improvement of vehicle safety and its convenience to drive a vehicle has resulted in the growth of the demand for vehicular electronic equipment. This trend requires additional power in the vehicle and thus makes prone to the increase of fuel consumption for vehicles equipped with internal combustion engines. To minimize this fuel consumption, an efficient energy management (EM) strategy for the electrical system of alternator and battery is required. This paper proposes a successful EM strategy based on the rule-based alternator control using predictive information. The proposed strategy reduces fuel consumption by charging batteries using the residual kinetic energy during deceleration. In particular, we predict electrical energy that is recovered by the residual energy using a Markov chain-based velocity prediction algorithm. The accommodation of predicted electrical energy and current vehicle information determines one of the three predefined control modes, such as charge, hold, and discharge, depending on vehicle driving states. This control mode determines the power generation from the alternator and controls the amount of torque to the vehicle electrical system. The proposed strategy is verified through simulation and experiment. The simulation with the new EM strategy is validated as comparing the operation difference with a conventional proportional-integral (PI) control algorithm under the same driver behaviors. Further validation in real vehicle driving experiment shows that fuel consumption was reduced by 2.1% compared to the conventional PI control algorithm.

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