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The emerging technology, transactive energy network, can allow multiple interconnected micro-grids (a.k.a. micro-grid clusters) to exchange energy for greater energy efficiency. Existing research has demonstrated that the micro-grid clusters can achieve some collective interests (e.g., minimizing total energy cost). However, some micro-grids may have to make sacrifices of their individual interests (e.g., increasing cost) for collective interests of the clusters. To bridge these research gaps, we propose four different transactive energy management models for micro-grid clusters where each micro-grid is allowed to have energy transactions with others. The first model focuses on maximizing collective interests, both the collective and individual interests are considered in the second model, and the last two models aim to maximize both the collective and individual interests. The performances of the proposed models are evaluated using a cluster of sixteen micro-grids with different energy profiles. It is demonstrated that 1) all of the four models can maximize the collective interests, 2) the third model can maximize the relative individual interests where each micro-grid can achieve the same percentage of cost savings as the clusters, and 3) the fourth model can maximize the absolute individual interests where each micro-grid can achieve the same amount of cost savings. [ABSTRACT FROM AUTHOR]

In the context of smart grid, the building can freely connect with other buildings to form clusters which are termed as building clusters to share energy. However, less study is conducted to develop optimal operation strategy for building clusters and evaluate the performance of building clusters in terms of different measures under different operation modes. Therefore, this research proposes a collaborative decision model to study the energy exchange among building clusters where the buildings share a combined cooling, heating and power system, thermal storage, and battery, and each building aims to minimize its energy cost, carbon emission or primary energy consumption. A collaborative decision framework is proposed to obtain Pareto operation decisions for the building clusters. We compare the performance of the collaborative strategy with the non-cooperative strategy where no energy sharing among the buildings. It is demonstrated that the collaborative strategy can significantly reduce energy cost, carbon emission and primary energy consumption under both grid connected and disconnected operation modes. The collaborative strategy under dynamic pricing plan is more cost effective than the strategy under flat pricing plan, which indicates that the collaborative strategy can motive buildings to more efficiently utilize the shared energy under dynamic pricing plan. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-objective optimization model for combined heat and power is proposed. [•] The model minimizes operational cost, primary energy consumption and CO2 emissions. [•] The proposed stochastic model guarantees the optimized system operation. [•] An incentive model is presented to assist the multi-objective decision analysis. [ABSTRACT FROM AUTHOR]

The demand on energy is uncertain and subject to change with time due to several factors including the emergence of new technology, entertainment, divergence of people's consumption habits, changing weather conditions, etc. Moreover, increases in energy demand are growing every day due to increases in world's population and growth of global economy, which substantially increase the chances of disruptions in power supply. This makes the security of power supply a more challenging task especially during seasons (e.g. summer and winter). This paper proposes a reinforcement learning model to address the uncertainties in power supply and demand by dispatching a set of electric vehicles to supply energy to different consumers at different locations. An electric vehicle is mounted with various energy resources (e.g., PV panel, energy storage) that share power generation units and storages among different consumers to power their premises to reduce energy costs. The performance of the reinforcement learning model is assessed under different configurations of consumers and electric vehicles, and compared to the results from CPLEX and three heuristic algorithms. The simulation results demonstrate that the reinforcement learning algorithm can reduce energy costs up to 22.05%, 22.57%, and 19.33% compared to the genetic algorithm, particle swarm optimization, and artificial fish swarm algorithm results, respectively. • An integrated routing and energy scheduling under uncertainty model is developed. • The model enables energy sharing among multiple EVs at spatial and temporal scales. • The proposed RL algorithm is more computational efficient than heuristic algorithms. • The proposed RL algorithm yield better quality results than heuristic algorithms. • The proposed algorithm has potential to be used for real-time decision making. [ABSTRACT FROM AUTHOR]

Due to the spatio-temporal complexity in energy consumption profiles of multiple consumers at different regions, it is expected that more cost savings and higher resiliency to power disruptions can be achieved using a network of mobile prosumers. In this paper, the prosumer is assumed to be an autonomous vehicle equipped with different distributed energy resources (e.g., solar panel, battery) that cannot only provide but also consume energy. An integrated vehicle routing and energy scheduling decision model is developed to adaptively dispatch vehicles to balance the temporally and spatially distributed energy requests subject to vehicle mobility constraints, and thus to maximally exploit the potentials of mobile prosumer network for cost savings and carbon emission reductions. The performances of the integrated decision model are evaluated using three metrics including operational costs, energy requested from power grids, and carbon emissions. The simulation results demonstrate that compared to disjoint vehicle routing and energy scheduling model, the proposed integrated decision model (i) is more efficient to shift energy loads at both temporal and spatial scales, and (ii) can save up to 38% of operational costs, reduce up to 29% of energy requested from power grids, and reduce up to 27% of carbon emissions. • An integrated vehicle routing and energy scheduling decision model is developed. • The proposed model enables more exible energy sharing among mobile prosumers at both spatial and temporal scales. • The proposed model can maximally exploit the potentials of mobile prosumers for cost reductions and energy savings. • The mobile prosumer network is more cost e_ective and environmental friendly. [ABSTRACT FROM AUTHOR]

Abstract The shipping industry has valued wind energy in the last decades due to the global increasingly severe environmental situation. The sail-assisted vessels have employed generous types of sails to exploit wind energy and reduce the energy consumption. To meet the ever-increasing energy saving requirements of sail-assisted vessels, this paper investigates the hard sail design optimization and energy efficiency enhancement problems for sail-assisted vessel. By using parametric section airfoil parametrization (PARSEC) method and particle swarm optimization algorithm, we fulfill the optimization of the wingsail aerodynamic design. The performance of our optimized airfoil has been verified by multi-point theory, computational fluid dynamics (CFD) examination, and energy efficiency design index (EEDI) calculation. The maximum lift-to-drag ratio of the coupled wingsails has increased by 10 % than that of the arc-shaped sail. And the vessel equipped with our coupled wingsails can remarkably reduce EEDI by 18.2 % and 4 % when compared to non-sails vessel, and the vessel equipped with variable-camber sail (VCS), respectively. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) can be utilized as a dynamically configurable dispersed energy storage in the vehicle-to-grid (V2G) and vehicle-to-building (V2B) operation mode to balance the energy demand between buildings and EVs. This paper proposes a mixed integer linear programming based collaborative decision model to study the energy sharing between a building and an EV charging station (CS). The building has its distributed generator, and electric and thermal energy storage, and the CS has its own renewable energy source. To model the V2G/V2B integration, we introduce three sets of decision variables to represent the energy exchange among building, CS and power grid. A set of parameters are introduced to model the driver behaviors, such as initial and desired state of charge level of EV battery, and available hours of EV, and sixteen different building categories (e.g., office, restaurant, hotel, warehouse, etc.) are studied. The impacts of driver behaviors and building categories on the economic performance of V2G/V2B integration are characterized and analyzed. The results from this research can recommend best V2G/V2B integration considering various driver behaviors and building categories which can provide valuable insight for smart community design. [ABSTRACT FROM AUTHOR]