According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users' willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers' costs and improving the sharing of the PV energy.

To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

This paper introduces a distributed algorithm for sparse load shifting in demand-side management with a focus on the scheduling problem of residential smart appliances. By the sparse load shifting strategy, customers' discomfort is reduced. Although there are many game theoretic models for the demand-side management problem, the computational efficiency of finding Nash equilibrium that globally minimizes the total energy consumption cost and the peak-to-average ratio is still an outstanding issue. We develop a bidirectional framework for solving the demand-side management problem in a distributed way to substantially improve the search efficiency. A Newton method is employed to accelerate the centralized coordination of demand side management strategies that superlinearly converge to a better Nash equilibrium minimizing the peak-to-average ratio. Furthermore, dual fast gradient and convex relaxation are applied to tackle the sub-problem for customers' best response, which is able to relieve customers' discomfort from load shifting or interrupting. Detailed results from illustrative case studies are presented and discussed, which shows the costs of energy consumption and daily peak demand by our algorithm are reduced. Finally, some conclusions are drawn.

This paper proposes a control framework, called pulse-modulated intermittent control, which unifies impulsive control and sampled control. Specifically, the concept of pulse function is introduced to characterize the control/rest intervals and the amplitude of the control. By choosing some specified functions as the pulse function, the proposed control scheme can be reduced to sampled control or impulsive control. The proposed control framework is applied to consensus problems of multiagent systems. Using discretization approaches and stability theory, several necessary and sufficient conditions are established to ensure the consensus of the controlled system. The results show that consensus depends not only on the network topology, the sampling period and the control gains, but also the pulse function. Moreover, a lower bound of the asymptotic convergence factor is derived as well. For a given pulse function and an undirected graph, an optimal control gain is designed to achieve the fastest convergence. In addition, impulsive control and sampled control are revisited in the proposed control framework. Finally, some numerical examples are given to verify the effectiveness of theoretical results.

The resource allocation problem is studied and reformulated by a distributed interior point method via a θ-logarithmic barrier. By the facilitation of the graph Laplacian, a fully distributed continuous-time multiagent system is developed for solving the problem. Specifically, to avoid high singularity of the θ-logarithmic barrier at boundary, an adaptive parameter switching strategy is introduced into this dynamical multiagent system. The convergence rate of the distributed algorithm is obtained. Moreover, a novel distributed primal-dual dynamical multiagent system is designed in a smart grid scenario to seek the saddle point of dynamical economic dispatch, which coincides with the optimal solution. The dual decomposition technique is applied to transform the optimization problem into easily solvable resource allocation subproblems with local inequality constraints. The good performance of the new dynamical systems is, respectively, verified by a numerical example and the IEEE six-bus test system-based simulations.

With the increase of vehicle travelling range and the decrease of vehicle expense, Electric Vehicles (EVs) are gaining popularity as a new transport option which offers a great opportunity for creating a smart city in terms of transportation electrification. However, the existing research of EV charger planning primarily concentrates on the optimal location seeking from a perspective of global optimization. To achieve a competitive strategy in future, a market-based mechanism is proposed for the problem of EV charger planning with a case study of Sydney where a predict-then-optimise diagram is introduced to predict the EV charging demand by a multirelation graph convolutional network (GCN) based encoderdecoder deep architecture and optimise the competitive resource allocation strategy for the charger planning through a Cournot competition game model. A new parallel computational algorithm is proposed to seek the Cournot competition equilibrium with the convergence analysis. Furthermore, the optimal EV charger sizing problem is considered to obtain each service provider’s EV charger number at each zone of the city. Experiments are implemented by using the Sydney public transportation dataset and the corresponding key economic indicators. The results show the effectiveness of our proposed approach which can fill the gap between data and the optimal EV charging infrastructure planning.