This paper investigates the effectiveness of the Artificial Neural Network (ANN) approach to short term load forecasting in electrical power systems. Using examples, the learning process and capabilities of a neural network in the prediction of peak load of the day are demonstrated. Different data normalizing approaches and input patterns are employed to exploit the correlation between historical load and temperatures and expected load patterns. A number of AN"s are included with emphasis given to their practical implementation for electrical power system control and planning purposes. The networks have been trained on actual power utility load data using a backpropagation Algorithm. The prospects for applying a combined solution using artificial neural networks and expert systems, called the expert network are also discussed. Consideration is given to expert networks as a more complete solution to the forecasting problem which neither system alone can provide.

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Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

With the growth of distributed generation (DG) and renewable energy resources the power sector is becoming more sophisticated, distributed generation technologies with its diverse impacts on power system is becoming attractive area for researchers. Reliability is one of the vital area in electric power system which defines continuous supply of power and customer satisfaction. Around the world many power generation and distribution companies conduct reliability tests to ensure continues supply of power to its customers. Uttermost reliability problems in power system are due to distribution network. In this research reliability analysis of distribution system is done. The interruption frequency and interruption duration increases as the distance of load points increase from feeder. Injection of single DG unit into distribution system increase reliability of distribution system, injecting multiple DG at different locations and near to load points in distribution network further increases reliability of distribution system, while introducing multiple DG at single location improves reliability of distribution system. The reliability of distribution system remains unchanged while varying the size of DG unit. Different reliability tests were done to find the optimum location to plant DG in distribution system. For these analyses distribution feeder bus 2 of RBTS is selected as case study. The distribution feeder is modeled in ETAP, ETAP is software tool used for electrical power system modeling, analysis, design, optimization, operation, control, and automation. These results can be helpful for power utilities and power producer companies to conduct reliability tests and to properly utilize the distributed generation sources for future expansion of power systems.

Making of smart grids puts mounting pressure on the nation's aging electric power transmission system. Just planting additional towers and stringing more line won't practice the nation's electric power transmission infrastructure to meet up the energy challenges ahead. Smart grids stand geared up to play a much larger role in the energy equation for reduction of transmission line losses with the range of technologies and methodologies now on hand. The FACTS controllers come out with the capability of enhancing transmission system control, reliability, and operation. Shunt Flexible AC Transmission Systems (FACTS) devices have been used in power systems since the 1970s for the improvement of its dynamic performance. This paper will discuss and express how Static Synchronous Compensator (STATCOM) has effectively been applied to power system for efficiently regulating system voltage and thus increase system load ability. This paper investigates the effects of (STATCOM) on voltage stability of a power system at different positions. STATCOM plays an important role in controlling the reactive power flow to the power network, when it is placed in a long transmission line. The simulation analysis of this paper can be used as guideline for power industry. The study is thereby simulated using the MATLAB/SIMULINK software and simulation results show that STATCOM is effective in midpoint voltage regulation on transmission line. In this paper comparison is also performed between STATCOM and SVC under fault condition and it is proved that STATCOM have the capacity to provide more capacitive power for the period of a fault than SVC. It is also displayed that STATCOM shows faster response than SVC.