Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey.

Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM’s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality.

This paper presents a systematic approach to selecting lithium iron phosphate (LFP) battery cells for electric vehicle (EV) applications, considering cost, volume, aging characteristics, and overall performance. A battery selection algorithm is developed to evaluate four different LFP battery cell models based on their long-term behavior in a 40 kWh battery pack. The algorithm integrates a vehicle energy consumption model, where battery performance is analyzed based on the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) over a 10-year simulated period, considering five driving cycles per day. In order to ensure a fair assessment, the model accounts for variations in battery pack weight as the sole influencing factor on vehicle dynamics. The results compare vehicle range, battery pack mass, cost, cell degradation, and volume for each battery option. The findings indicate that the A123 Systems ANR 26650m1 battery cell offers the best trade-off between longevity and cost-effectiveness, making it a highly suitable choice for durable and efficient EV battery packs. This study underscores the importance of the most suitable battery selection in designing cost-effective, long-lasting EV energy storage solutions.