Peak Time Load Research Articles

Maximum Self-Consumption Planning Framework Considering Energy Management System for Optimal Photovoltaic-Battery System in Distribution Network

Power production from photovoltaic (PV) has advanced as Renewable Energy Resources (RES) power technology and is now a worthwhile alternative to conventional power generating. To effectively stabilize the process of connecting to the grid and increase power controllability owing to changing PV power production, energy storage systems (ESS), with their flexible charging and discharging capabilities, are most often used to cooperate with RES power generation. However, ESS's rapid rate of charging and discharging might cause it to age more quickly than it should and reduce its lifespan. Therefore, this study proposed an effective planning framework of optimal penetration of PV-ESS system associated with effective energy management systems (EMS). The proposed framework, the optimal PV size, PV location and maximum State of Charge (SOCmax) are determined. The fixed minimum State of Charge (SOCmin) based on load conditions is applied to minimize the degradation while maximizing PV self-consumption. The result shows that when applying different SOCmin during at off-peak and peak time load demand, the degradation cost is reduced to 19.40%.

Hybrid PV-Battery System Evaluation in Baghdad: ICOE and NPV Analysis using SAM Software

In this study, The System Advisor Model (SAM) simulation software has been used to analyze a Hybrid PV-Battery System in a residential cite in Iraq-Baghdad. The proposed system is 5kW which is affordable and applicable from the cost and required area points respectively. The monthly averaged electrical load is approximately calculated. The performance ratio for the system is 0.75 and about 6834 KWh is generated by the system per year. The results showed that the initial capital cost is 4,167.01$ which is returned after six years, LCOE Levelized cost of energy real is 7.66 ¢/kWh and Net present value is $4,649 made the system are very economical/no maintenance project and can be hooked to any the peak time load.

Peak Time Load Management in Domestic Sector

The electricity demand is increasing day by day and leads do increase in generation and power transmission. India consumes about 3.4% of world energy consumption. The energy demand has grown at the average of 3.6% per annum over the past 30 years. Usually the demand for the electricity increases abruptly during peak hours. So it is necessary to monitor the loads during peak hours in order to provide energy sufficiently. The government has taken steps to reduce power consumption in industrial sector. Demand reduction can also possible in domestic sector. This Paper presents a load management model for domestic houses. Every house has a maximum demand limit for essential loads. During peak hours, if the consumer consumes power more than the preset limit,, based on the priority of load the power supply to the load will be turned off automatically and by using the cloud, the data can be visualized and analyzed. The power consumption during peak time is updated in cloud using NODEMCU and Thingspeak. The information can also be used for load forecasting

Reducing the carbon emission by early prediction of peak time load in a data center

Reducing the carbon emission by early prediction of peak time load in a data center

A Theoretical Detailed Analysis for a Proposed 5kW PV Grid-Connected System Installed in Iraq Using PVsyst Tool

In this study, PVsyst simulation software is used to analyze a PV grid tied system in a typical primary school in Iraq. The proposed system is 5kW which is affordable and applicable from the cost and required area points respectively. The monthly averaged electrical load for a typical school is approximately calculated. The system simulation has been done for thirteen Iraqi provinces while it is described and its performance is analyzed in details for Baghdad city as an example. The performance ratio for the system in Baghdad is 0.825 and about 9.82MWhr is generated by the system per year, 62.7% is consumed by the load and the remaining is injected to the national grid. The results showed that the initial capital cost is 5,442$ which is returned after five and a half years. The cost of 1 kWhr if the system operates for 10 years is 0.058$. This study clearly demonstrates that photovoltaic power system can effectively assist the peak load on the grid. In addition, these systems are very economical/no maintenance project and can be hooked to any the peak time load.

Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

Demand response (DR) program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA)-based artificial neural network (ANN) to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM), are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO) based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

Development of a three-part time-of-day electrical energy tariff

This paper presents an analytical technique for determining peak, offpeak and midpeak hours tariffs for various durations of these three periods. The technique considers the existing flat rate tariff, an hourly generation scenario of a utility on an average yearly demand day, the weighted average life time, annutised capital cost and fuel cost of base and peak load plants, and a break even point condition in the utility's revenue. The developed tariff has been applied to 30 representative industrial consumers served by the Bangladesh Power Development Board (BPDB) to estimate the minimum possible shift in consumption pattern and hence corresponding reduction in peak generation capacity requirement as well as peak time load shedding by BPDB. © 1998 John Wiley & Sons, Ltd.

Development of a three‐part time‐of‐day electrical energy tariff

This paper presents an analytical technique for determining peak, offpeak and midpeak hours tariffs for various durations of these three periods. The technique considers the existing flat rate tariff, an hourly generation scenario of a utility on an average yearly demand day, the weighted average life time, annutised capital cost and fuel cost of base and peak load plants, and a break even point condition in the utility's revenue. The developed tariff has been applied to 30 representative industrial consumers served by the Bangladesh Power Development Board (BPDB) to estimate the minimum possible shift in consumption pattern and hence corresponding reduction in peak generation capacity requirement as well as peak time load shedding by BPDB. © 1998 John Wiley & Sons, Ltd.