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Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand, double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model and formulation of the phenomena as a constrained optimization problem. Optimization problem is then solved by using differential evolution method and particle swarm optimization algorithm. The proposed algorithm is applied to IEEE test systems. Computational accuracies of differential evolution based solutions and particle swarm optimization based solutions are discussed for IEEE 30 Bus Test System and IEEE 118 Bus Test System applications. IEEE 14 Bus Test System, IEEE 30 Bus Test System, IEEE 57 Bus Test System, IEEE 118 Bus Test System and IEEE 300 Bus Test System simulation results are compared to AC load flows in terms of computational speed. Finally the performance of the proposed method is analyzed for different outage configurations. [ABSTRACT FROM AUTHOR]

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD. • Optimal DER planning and ESS management in EDNs using a three-dimensional multiobjective optimization framework • DN Reliability improvement together with power losses and annual cost minimization. • 72 h optimization period to account the seasonal characteristics of the load and the DGs. • Real load curve and wind and solar radiation data of Turkish territory. • Multi objective sine–cosine algorithm providing better Pareto fronts according to two performance indices. [ABSTRACT FROM AUTHOR]

High penetration of distributed generation (DG) in secondary (low-voltage) distribution networks (SDN) is one of the primary challenges of network operators. Particularly in radial LV networks, renewable intermittency and bidirectional power flow may cause critical problems such as under/over voltages, line overloads and high energy losses. System operators usually consider the worst-case scenarios for the assessment of the impacts of new integrations and usually they prefer passive methods to mitigate the negative effects. However, analysis of daily operation with high resolution, stochastic DG supply and residential demand profiles can reveal both the occurrence rates and the severities of the critical events, while paving the way for the development of active mitigation methods. This study is focused on detailed daily operation analysis of SDNs with renewable-based microgrids. Seasonal DG output and residential demand profiles throughout a day were simulated using a virtual test bed to investigate the critical cases. [ABSTRACT FROM AUTHOR]

• A gamification platform to foster energy efficient behaviors in office buildings. • The gamification platform is a mobile application with interactive dashboards. • The mobile gamification platform was demonstrated in an office building. • The demonstration results suggest electricity savings of 20%. Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%. [ABSTRACT FROM AUTHOR]