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Applying Cogeneration To a Facility
Published in Bernard F. Kolanowski, Small-scale Cogeneration Handbook, 2021
Therefore, the first part of the analysis is to break out the energy costs from the demand costs and list them by month along with the energy and demand used. Then, a load profile must be considered. A load profile is an hour by hour picture of when the electricity is used within the facility. If the facility opens at 6:00 a.m. and closes at 10:00 p.m., it is reasonable to assume most of its electricity is consumed within those hours. But, if air-conditioning units are left on all night or swimmingpool circulating pumps run all night and day, plus security lighting and whatever else may still be in use, the electric load should be looked at for the entire 24-hour day. The daytime load may support a 120-kilowatt cogeneration unit, but the night time load may fall to only 20 kilowatts of demand. Is it worth running the large cogenerator during the night only displacing 20 kilowatts of electricity and selling or giving back to the utility the other 100 kilowatts? Probably not. Therefore, prudence suggests shutting down the cogenerator when the facility closes and restarting it when the facility opens.
Convergence of Technologies and IT/OT Integration
Published in Stuart Borlase, Smart Grids, 2018
Stuart Borlase, Michael Covarrubias, Jim Horstman, Greg Robinson, Stuart Borlase, John Chowdhury, Greg Robinson, Tim Taylor
The Time-Series Data Store is optimized for handling time series data, arrays of numbers indexed by time (a date time or a date time range). A time series of energy consumption can be used for understanding a load profile. A database that can correctly, reliably, and efficiently implement query operations is typically specialized for time-series data. Software with complex logic or business rules and high transaction volume for time series data may not be practical with traditional relational database management systems. Flat file databases are not a viable option either, if the data and transaction volume reach a maximum threshold. Queries for historical data, replete with time ranges and roll ups and arbitrary time zone conversions, are more difficult in a relational database. Database that joins across multiple time series data sets is typically only practical when the time tag associated with each data entry spans the same set of discrete times for all data sets across which the join is performed.
Electric Energy Systems—An Overview
Published in Antonio Gómez-Expósito, Antonio J. Conejo, Claudio Cañizares, Electric Energy Systems, 2017
Ignacio J. Pérez-Arriaga, Hugh Rudnick, Michel Rivier Abbad
Consumption is characterized by a variety of items from the technical standpoint. The two most important items are power and energy. Power, measured in watts (W), is the energy (Wh) required per unit of time. Power, therefore, is the instantaneous energy consumed. Since electric power is not stored, electric facilities must be designed to withstand the maximum instantaneous energy consumed, in other words, to withstand the maximum power load in the system throughout the consumption cycle. Therefore, not only the total electric capacity needed, but the demand profile over time is especially relevant to characterize consumption. Such profiles, known as load curves, represent power consumed as a function of time. It may be readily deduced that a given value of energy consumed may have a number of related load profiles. Some may be flat, indicating very constant electricity consumption over time, while others may have one or several very steep valleys or peaks, denoting very variable demand. An aluminium plant working around the clock 365 days a year and a factory operating at full capacity only during the daytime on weekdays would exemplify these two types of profiles. Load profiles commonly generate repetitive patterns over time. Thus, for instance, weekday demand is normally very uniform, as is the weekly load during a given season. Therefore, depending on the timescale considered, the load profile to be used may be daily, weekly, monthly, seasonal, yearly, or even multiyearly. Load profiles also have economic relevance, as will be seen in the discussion below: for any given demand level, it is less expensive to cover a flat than a spiked load profile. For this reason, load curves constitute one of the most relevant parameters considered in the methods used to set tariffs.
Novel Metaheuristic Optimizers Based Load Shifting and Flexible Load Curve Techniques for Demand-side Load Management
Published in Smart Science, 2023
Ashokkumar Parmar, Pranav Darji
To reduce imported energy as well as to improve efficiency and reliability, renewable resources are integrated at the demand side, and demand-side resource management is implemented to ensure demand and supply balance. Demand-side load management is adopted to change the load profile when limited energy resources are available, or the available infrastructure is to be used most efficiently. In demand-side load management, the end user’s activities are controlled by rescheduling their operations, encouraging customers to operate some of their appliances in the periods when considerable renewable energy is available and discourages customers for energy consumption during the periods of generation crisis. Demand-side load management is used to changes in the original electrical energy usage patterns of customers according to changes in the market prices (electricity cost); hence, these usage pattern changes reduce the overall cost per unit of electricity [8–10].
A novel stochastic model for hourly electricity load profile analysis of rural districts in Fujian, China
Published in Science and Technology for the Built Environment, 2022
Bing Zhou, Xiao Wang, Da Yan, Jieyan Xu, Xuyuan Kang, Zheng Chen, Tianyi Hao
The methods used for electricity load research are summarized in Table 1. Clustering is commonly used and prominent in the extraction of typical load profiles in electricity load profile analysis. However, this method is limited by the lack of variation and randomness when the influential factors of the load change. Regression is a practical method to explore variations in electricity load, but this method may not be capable of depicting the hourly electricity load due to its randomness. For electricity load simulation, the probability distribution method reflects the randomness of the electricity load and provides the possibility of simulating extreme electricity use. Nevertheless, the probability distribution method is insufficient for describing the determined pattern of load variation and load profile. A combined method of the three methods is required to improve the electricity load simulation for energy system design.
Optimal Placement and Sizing for Solar Farm with Economic Evaluation, Power Line Loss and Energy Consumption Reduction
Published in IETE Journal of Research, 2022
Sajjad Ahmadnia, Ehsan Tafehi, Fatemeh Shakhsi Dastgahian
Figures 3 and 4 show the difference between the PV output power for each month according to Mashhad's geological data for PVs with and without the Sun-tracker, respectively. For PV with the Sun-tracker, when the sun is faced directly to the panel the output power is at the highest level. Figures 5–10 show the load profile with PV panel output in kW in April, July and October to illustrate spring, summer and autumn, respectively. The x axis represents the hour and y axis represents voltage in p.u. Illustrated models are for both PV types, with and without the Sun-tracker. Load profile is the amount of consumed power during a day and it is different for each month and varies according to the consumer's habit. As the load profile illustrated in Figures 5–10, there are two separate peak loads during a day. One in around 11 am. and the important one happens at around 7 pm. when the Sun is setting.