• Energy Research

Plug-in electric vehicles are widely acknowledged as an effective tool for numerous environmental and economic concerns. In this article, a novel model for the planning of fast-charging stations is established based on a data-driven distributionally robust optimization approach, which aims to minimize the expected planning cost for both transportation network and distribution network. $\phi $ -divergence, a statistical measure, is utilized to establish the serviceability constraints. On the other hand, a modified capacitated flow refueling location model is employed to develop the location constraints. In addition, ac power flow constraints are developed to model the operation of DN with the penetrations of PEVs. Finally, a case study is illustrated to validate the proposed planning model.

Abstract Elman network is a classical recurrent neural network with an internal delay feedback. In this paper, we propose a recalling-enhanced recurrent neural network (RERNN) which has a selective memory property. In addition, an improved conjugate algorithm with generalized Armijo search technique that speeds up the convergence rate is used to train the RERNN model. Further enhancement performance is achieved with adaptive learning coefficients. Finally, we prove weak and strong convergence of the presented algorithm. In other words, as the number of training steps increases, the following has been established for RERNN: (1) the gradient norm of the error function with respect to the weight vectors converges to zero, (2) the weight sequence approaches a fixed optimal point. We have carried out a number of simulations to illustrate and verify the theoretical results that demonstrate the efficiency of the proposed algorithm.

In the multimicrogrids environment, this brief proposes a distributed power management strategy to minimize a sum of generation costs function subject to generator constraints, which includes the supply-demand balance constraint, the individual constraint, the capacity constraint, and the ramp-rate constraint. First, under the framework of discontinuous dynamical system, a distributed primal–dual consensus algorithm is discussed. Then, using the property of Laplacian matrix, it is proven that the algorithm is any-time, which indicates that supply-demand balance is not disturbed. Based on theory of dynamical system, the proposed algorithm is proven to be stable, and the optimization capabilities of the distributed primal–dual consensus algorithm are also discussed. Finally, case studies based on IEEE 30-bus, IEEE-57 bus, and IEEE 300-bus system show the effectiveness and performance of the proposed distributed primal–dual consensus algorithm.

Realistic industrial process is usually a dynamic process with uncertainty. Chance constraints are applicable to industrial process modeling under uncertain conditions, where constraints cannot be strictly met, or need not be fully met. Therefore, chance constrained dynamic optimization (CCDO) formulation is available to address realistic industrial process issues. Because of the dynamic and uncertainty, chance constrained dynamic optimization problems (CCDOPs) arising from practical industries are hard to cope with. In this article, a novel CCDO method is proposed to resolve this issue, where an adaptive sample average approximation method, a control vector parameterization method, and a state constraint handling strategy are integrated. Specially, a hybrid intelligent optimization algorithm is introduced to realize a global and efficient optimization performance. The proposed method is applied to CCDOPs modified by dynamic optimization standard test functions and industrial experiments to demonstrate its effectiveness. The experimental results show that the proposed method has good performance in solving CCDOPs.

Abstract This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.

In this paper, we discuss a class of distributed constrained optimization problems in power systems where the target is to optimize the sum of all agents’ local convex objective functions over a general unbalanced directed communication network. Each local convex objective function is known exclusively to a single agent, and the agents’ variables are constrained to global coupling linear constraint and individual box constraints. To collaboratively solve the optimization problems, existing distributed methods mostly require the communication network to be balanced or have the knowledge of in-neighbors’ out-degree for all agents, which are quite restrictive and hardly inevitable in practical applications. In contrast, we investigate a novel distributed primal-dual augmented (sub)gradient algorithm which utilizes a row-stochastic matrix (does not need each agent to know its in-neighbors out-degree) and employs uncoordinated step-sizes, and yet exactly converges to the optimal solution over a general unbalanced directed communication network. Under the assumptions of the strong convexity and smoothness on the aggregate objective functions, it is proved that the algorithm geometrically converges to the optimal solution if the uncoordinated step-sizes do not exceed the upper bound. An explicit analysis for the convergence rate of the proposed algorithm is also characterized. To manifest effectiveness and applicability of the proposed algorithm, three case studies are presented to solve two practical problems in power systems.

This paper presents a microgrid system model considering three types of load and the user’s satisfaction function. The objective function with mixed zero-one programming is used to maximize every user’s profit and satisfaction in the way of the demand response management under real-time price. An energy function is used to transform the constrained problem into an unconstrained problem, and two neural networks are used to find the local optimal solutions of the objective function with different rates of convergence. A neurodynamic approach is used to combine the neural networks with the particle swarm optimization to find the global optimal solution of the objective function. The simulation results show that the combined approach is effective in solving the optimal problem.