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The fundamental roots of micro-grids are different types of renewable energy sources. There are two broad and distinctive control set ups for power systems. They are centralized and decentralized (hierarchical) controls. In market models of micro-grids there are normally groups of electricity sources and loads that operate in synch with a centralized grid or macro-grid. This paper studies the functionality...
Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires...
The short-term load forecasting study is characterized by an estimative of the consumption pattern ranging from a day to a few months ahead, related to the operation planning. In Smart grid concepts, this study also is important, but the quality of actual load forecasting methods must be improved, due new aspects like DG and Intermittent Loads. The objective of this work is develop a model to multi...
The promising benefits of the renewable sources based on distributed generation are pushing the future energy markets to invest more into the available renewable systems. This research will focus on integrating available renewable energy resources in Kingdom of Saudi Arabia in the electric grid to minimize the energy production from fossil fuels through continuous prediction and forecast of demand...
Energy Storage Systems will play crucial role in controlling the grid of the future when increased penetration of renewable energy sources will take place. Especially batteries are expected to occupy a considerable share of the total energy storage market by simultaneously providing services to different stakeholders such as energy producers, transmission/distribution operators, residential, commercial...
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
To achieve climate and carbonization goals, electricity grid participants, such as buildings, must reduce their footprint trough renewable generation. Introducing storages can help buffering the fluctuating nature of renewable energy sources but only with future knowledge of consumption and generation, can batteries be scaled sensibly to economically viable options. An efficient energy management...
Grid modernization has brought in various types of active demand, and intermittent and distributed generation resources to challenge the traditional power system planning and operation practices. As a result, more and more decision making processes rely on probabilistic forecasts as an input. While residual simulation has been recognized as one way to generate probabilistic load forecasts, the research...
Power load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was...
Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity...
In a building office, an air-conditioning system is one of the systems that contributes most to the electrical energy expense. The ability to predict the short-term electrical energy consumption in an air-conditioning environment can provide valuable information in controlling electrical appliance usages so that the overall energy consumption can be kept at an acceptable level for most of the time...
The Electric load supply industry requires forecasts with lead times that range from short terms (a few hours, or days ahead) to long terms (up to 10 years ahead). Load forecasting is a complex task because of high non-linearity relation among load variables and load exhibits several levels of seasonality. This paper presents the effect of Hijri calendar on load forecasting. Hijri calendar could be...
The paper presents application of STATISTICA v6.0 and STATISTICA NEURAL NETWORKS software for electrical load forecasting. Relevance of forecasting is influenced by the fact that extraction of minerals in oil and gas industry is increasing. As oil extraction and transportation is very power intensive, the problem of load growth has arisen. Then, a task for forecasting of load growth occurs. The results...
Buildings are one of the major sources of greenhouse gas emissions and electricity consumption in urban areas all around the world. The load demand of large buildings is highly uncertain due to large penetration of solar PV. As a result, it leads to serious power system stability and quality issues for network operators and energy managers. Therefore, accurately forecast the load demand of buildings...
This paper presents different methods to solve short-term load forecasting problem in smart grids. Smart grid, an electrical network can be monitored and managed. Effective and efficient use of energy and a low-cost planning-oriented management are required in smart grids. One of the most important helpers for power management is to forecast load correctly. Load forecasting, demand response and energy...
This paper presents a comparative study of short-term load forecasting using Artificial Intelligence (AI) and the conventional approach. A feed-forward, multilayer artificial neural network (ANN) was employed to provide a 24-hour load demand forecast. In this model, historical data, weather information, day types and special calendar days were considered. The forecasted results using AI were compared...
Load forecast is becoming currently more fundamental in planning, operating and controlling of modern electric power systems. Nowadays the load peak forecast is also important, because it is of great interest in economy stability and improvement in the electrical systems. This paper presents an approach for load forecasting in the medium voltage distribution network in Portugal. The forecast horizon...
The increase of renewable non-programmable production and the necessity to locally self-consume the produced energy led to utilize ever more storage systems. To correctly utilize storage systems, an opportune management method has to be utilized. This paper implements a multi-period management method for storage devices, using different management strategies. The method aims to minimize the total...
This paper compares four practical methods for electricity generation forecasting of grid-connected Photovoltaic (PV) plants, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling, SARIMAX modeling (SARIMA modeling with exogenous factor), modified SARIMA modeling, as a result of an a posteriori modification of the SARIMA model, and ANN-based modeling. Interesting results regarding...
This paper describes a Neural Network application to reduce the computational complexities in a smart grid environment by predicting peak demand and losses for the next operating day. Mitigating peak demands and losses could significantly reduce outage risks and increase costs savings, hence improving the reliability and efficiency of Electrical Service Providers (ESPs).
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