Abstract
This paper presents a reliability-based approach for the design and deployment of an energy management system (EMS) by using 'smart' applications, such as energy storage (ES), to control battery power output in residential dwellings, and thus improve distribution-network reliability performance. The state of charge (SOC) of the battery system is designed based on time-varying electricity tariff, load demand and solar photovoltaic (PV) generation data to investigate a realistic test-case scenario. Additionally, a typical MV/LV urban distribution system is fully modelled and scripted to investigate the potential benefits that 'smart' interventions can offer to customers' quality of power supply. In this research, Monte-Carlo simulation method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Accordingly, the reliability-based effects from SOC variation in batteries are compared with an uncontrolled microgeneration (MG) scenario, by using different PV penetration levels to justify the value of control. The benefits are assessed through standard reliability indices measuring frequency and duration of power interruptions and most importantly, the energy not supplied to customers during sustained interruptions.

Abstract
Microgrid, as an emerging small-scale power system comprising a range of power sources, power electronic interfaces, loads, storage units, and being able to supply remote areas or local communities, either can be operated in islanded or grid-connected mode. Based on this concept, this paper proposes the scalability assessment and day-ahead optimization, with time-varying load and time-of-use tariff data in 48 time-periods, for multiple microgrids applied in the accommodation area in a UK university, based on an existing microgrid test system currently under investigation in its Smart Grid Laboratory. Four different scenarios, including weekdays and weekends over two seasons (summer and winter), are analyzed to achieve the optimal scheduling of the microgrid technologies. In addition, a long-term planning assessment, on optimization over 20 years, is presented to discuss the influence of microgrids' power component depreciation and life span on total energy costs and savings.

Abstract
This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), mostly intermittent in nature, might have on the reliability performance of distribution networks. A test distribution system based on a typical MV/LV urban distribution network in the UK is fully modelled and controlled to investigate the potential benefits that local renewables and energy storage can offer to the quality of power supply to customers. In this analysis, the conventional Monte Carlo method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs, with photovoltaic (PV) systems as key enablers, is quantified in terms of the effect of network outages. A range of smart grid functionalities is analysed and their benefits are assessed through standard reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions.

Abstract
The growing number of energy consuming devices, together with the increasing demand for energy, has led to an energy crisis in the world today. As electricity is an important factor for the development in the economic growth. At this time, the growing population will bring disaster situations relating to electrical energy and pollution; it will affect both the economy and the citizens and make a barrier in the path to complete with developed countries. In order to overcome the epileptic situation of electrical energy and make our environment clean and green. First, we need to give opportunities for renewable energy to address electricity generation. Second, there is no other way to reduce complexity and eliminate energy problem without using solar energy. The solar energy which is renewable energy, environmental friendly and free of cost but still we face difficulty by complete harnessing of solar energy because of variation of irradiation and temperature to get constant point at the output we will use PI controller.Moreover, there is different classification of inverter we used "PWM inverter" that can take steady DC voltage PWM inverter can take in a steady dc voltage. So as to feed solar voltage to the boost converter to step-up solar voltage we used PI controller to control boost converter and get constant voltage at the output the we feed this boost DC voltage to the 3phase PWM inverter to converter into three phase AC voltage. Finally, the inverter should direct the extent and the recurrence of AC output voltages, and the diode rectifiers are required to settle the line-to-line voltage.

Abstract
This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

Abstract
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.