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The realization of the Smart Grid vision will change the way of producing and distributing electrical energy. It paves the road for end-users to become pro-active in the distribution system and, equipped with renewable energy generators such as a photovoltaic panel, to become a so called "prosumer". The prosumer is engaged in both energy production and consumption. Prosumers' energy can be transmitted and exchanged as a commodity between end-users, disrupting the traditional utility model. The appeal of such scenario lies in the engagement of the end user, in facilitating the introduction and optimization of renewables, and in engaging the end-user in its energy management. To facilitate the transition to a prosumers' governed grid, we propose a novel strategy for optimizing decentralized energy exchange in digitalized power grids, i.e., the Smart Grid. The strategy considers prosumer's involvement, energy loss of delivery, network topology, and physical constraints of distribution networks. To evaluate the solution, we build a simulation program and design three meaningful evaluation cases according to different energy flow patterns. The simulation results indicate that, compared to traditional power distribution system, the maximum reduction of energy loss, energy costs, energy provided by the electric utility based using the proposed strategy can reach 51%, 66%, 97.5%, depending on the strategy. Moreover, the proportion of energy self-satisfaction approaches reaches 98%.

This paper presents the transient model of a two-bed adsorption cooling system performed in the SIMULINK platform. The inlet chilled water temperature in the evaporator, temperature of cooling water and hot water temperature of the adsorbent bed and its effect on systems coefficient of performance, refrigeration effect and specific cooling power have been studied and presented. It is observed that the systems coefficient of performance is 0.57 when the inlet hot water temperature about 80 °C. In this study, the optimum cooling power and systems coefficient of performance are also determined in terms of the phase time, shifting duration and hot water inflow temperature. The results indicates that the cooling water and hot water inlet temperatures significantly affects the coefficient of performance, specific cooling power and cooling power of the system. The effect of mass flow rate on the cooler efficiency is also presented. A two bed adsorption system of capacity 13.5 kW having an evaporator and condenser temperatures of 6°C and 28°C, respectively, are considered for the present investigation. The adsorbent mass considered is 45 kg with a shifting duration of 20 sec. The result of this study gives the basis for performance optimization of a practical continuous operating vapour adsorption cooler.

The vision of the future Smart Grid considers end-users connected to it as both consuming and generating energy. Equipped with small-scale renewable energy generators and storage systems, end-users, also known as prosumers, engage in a local energy market for procuring and selling energy, in turn disrupting the traditional utility model. The appeal of this vision lies in the engagement of end-users, in facilitating the introduction and optimization of renewable energy sources, with the overall expectation of optimizing the global energy generation and distribution process. To handle the peer-to-peer energy exchange and distributed energy generation in the digitalized Smart Grid, we proposed an optimization strategy. In the present work, we propose a Monte Carlo based simulation model to investigate the role of the topology in facilitating the peer-to-peer energy exchanges and distributed energy generation. We consider a 37-node distribution network and evaluate four topological models: radial, complete graph, random graph, and small-world. The results indicate that the random graph model is better than other models in reducing the average delivery path length and energy losses in the energy transfer between providers and consumers. The small-world model has higher efficiency than other models in reducing the maximum power load in the distribution network and the cost of buying energy for end-users. We scale up the investigation by considering a 100-node network and evaluate the random graph and the small-world models by varying the rewiring probabilities. The results show that the small-world model outperforms the random graph model on most efficiency metrics, even when considering infrastructural costs. This work provides the foundation for a decision support system for analysis and high level planning of the distribution network.

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