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This paper proposes a cyber-physical active distribution system (CPADS) planning method, which considers the economic aspect and overall performance in the context of distributed generations (DGs) interconnection. First, the composition of CPADS is introduced for analyzing the effect of cyber system on the planning goals and constraints, based on which the dependence of the physical system on the cyber system is investigated. Then, the framework of CPADS planning is proposed by comparing it with active distribution system (ADS). Second, the planning is modeled to search for the appropriate DGs capacities, communication mode, and voltage control frequency with the constraints including performance, construction investment, and out-of-limit voltage probability, aiming at minimizing the construction and operation cost of cyber-physical systems and maximizing the DG penetration level. After that, the bi-layer multi-objective programming without considering the random failure of cyber system is adopted to derive the optimal solution set. Monte Carlo Simulation is then performed to solve for the out-of-limit voltage probability due to cyber failures, based on which the final scheme is obtained. Simulations are conducted on the modified IEEE 33-node distribution system. The results verify the effectiveness of the proposed method.

Industrial wireless sensor networks have been widely deployed in many industrial systems. The main communication paradigm of such systems, known as convergecast, is to converge sensing data to a centralized manager. The rapid and reliable data convergecast is essential to the industrial production. Multiple radio interfaces on a network device and convergecast scheduling algorithms can effectively reduce convergecast delay. Existing works confine to the convergecast based on linear- and tree-based routing. Compared to the two routing schemes, graph routing is more reliable. Although the graph routing gains more popularity in industrial networks due to its better reliability, few works have addressed its temporality performance. On the other hand, the number of radio interfaces also impacts on the convergecast delay. In this paper, we present a holistic framework to solve how to use multiple radio interfaces to converge data. First, we propose a convergecast scheduling algorithm for industrial wireless sensor networks with multiple radio interfaces. Second, based on our proposed scheduling algorithm, we propose an optimal algorithm and a fast heuristic algorithm to minimize the number of radio interfaces under the temporality constraint of industrial production. Evaluations show that all our algorithms perform closely to the optimal solution.

One key issue in designing battery-powered wireless sensor networks is to properly control the energy consumption of the sensor nodes in order to prolong their operation time (i.e. lifetime ). In this article, we present a real-life application of wireless sensor networks in trains to monitor the goods conditions in a long-distance transportation. We study the wireless sensor network deployment problem in developing a monitoring system with the goal of maximizing the network lifetime under constraints derived from the real application scenario. The key technical problem to solve is to determine the sensor placement and the transmission level for each sensor node, as well as the appropriate number of sensor nodes. We first formulate the problem with a realistic discrete power model as a mixed integer linear programming problem. Then, a two-step efficient deployment heuristic is proposed to satisfy these constraints step by step. The evaluation results indicate that the proposed heuristic performs almost the same as the optimal mixed integer linear programming solution. Moreover, the wireless sensor network with appropriate number of nodes can improve its lifetime up to 10.6% for a train with 80 boxcars. We also discussed a tested experiment in a laboratory environment, as well as the real implementation of the whole monitoring system.

Demand response is considered as a valuable functionality of the power grid and its potential impacts continue expanding with grid modernization. Colocation data centers (simply called colocation) are recognized as a notably promising resource for demand response due to their high power demand and remarkable potential in demand management. A major challenge of colocation demand response is the split incentive, that is, colocation operators desire demand response for financial compensation while tenants may not embrace demand response due to lack of incentives. To address this challenge, we study a hierarchical demand response scheme, where tenants within a colocation respond to the colocation operator while the operator interacts with the electric power company. We propose that twolevel marketing is suitable to this hierarchical scheme, and design a new market mechanism that results in a win-win situation for the operator and tenants. Specially, tenants who chooses to reduce power demand obtain financial rewards from the operator, while the operator receives financial compensation from the electric power company due to its tenants' demand reduction. An appealing feature of the mechanism is that it provably converges to a unique equilibrium solution. At the equilibrium, neither the operator or tenants can improve their individual economic performance by changing their own strategies. We evaluate the designed mechanism via detailed simulations and the results show the efficacy and validate the theoretical analysis for the mechanism.

Wireless sensor networks (WSNs) have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality). In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones.

Up till now, the high penetration of intermittent distributed generation (DG) has posed great challenges to the planning and operation of the grid. To achieve the best balance between the economic cost and the acceptable capacity of DG, this paper proposes a new integrated planning method of active distribution network (ADN) while considering decentralized voltage control. The technical framework and control strategy of voltage control system are set up. An integrated planning model under the constraint of maintaining voltage acceptable is implemented, whose objectives are comprehensive cost minimization and clean energy utilization maximization. The simulations are conducted using the Multi-objective Differential Evolution Algorithm (MODE). IEEE 33-node test feeder is employed with 4 DGs participating in the voltage control. The results demonstrate that the proposed approach is able to connect larger distributed generators and improve distribution network's economic benefits while maintaining voltage within the statutory limits.

Compared to electric vehicle (EV) charging mode, battery swapping mode can realize concentrated and orderly charging. Therefore battery swapping stations (BSS) can participate in the demand side management (DSM) as an integrated form. In this context, a new method to plan the capacity and location of BSS for EV, considering DSM, is proposed in this paper. Firstly, based on the original charging power of BSS with the rule of “First-In First-Out”, a bi-level optimal configuration model of BSS, in which net profit of BSS is maximized in the upper model and operating cost of Distribution Company is minimized in the lower model, is developed to decide the rated power, number of batteries, contract pricing and dispatched power of BSS for DSM. Then, the optimal locating model of BSS with the objective of minimizing network loss is built. A mesh adaptive direct search algorithm with YALMIP toolbox is applied to optimize the bi-level model. Simulation calculation was carried on IEEE-33 nodes distribution system and the results show that participating in DSM can improve the economic benefits of both BSS and distribution network and promote the consumption of distributed generation, verifying the feasibility and effectiveness of the proposed method.

With the rich in resources of solar energy and rapidly development of the new energy resources-based generation, the maximization of photovoltaic (PV) generation consumption has been paid much attention to. High voltage problem is one of the major limits to high penetration of PV station connected to power grid. Several voltage control strategies are proposed to improve voltage profile. An optimization method is proposed for computing the maximum penetration level of PV, taking active voltage regulation into account. Computer simulations have been performed on a 33-nodes distribution system to demonstrate the effectiveness of the proposed voltage control schemes at different operating conditions in increasing the maximum power generation of PV.

Up till now, the high penetration of intermittent distributed generation (DG) has posed great challenges to the planning and operation of the grid. To achieve the best balance between economic cost and acceptable capacity of DG, this paper proposes a new integrated planning method of the active distribution network while considering voltage control cost. Firstly, characteristics of decentralized and centralized voltage control methods were analyzed. The technical frameworks, voltage control strategies and economical models of different voltage control systems were put forward. Then, an integrated planning model with objectives to minimize the comprehensive cost and maximize clean energy utilization under the constraint of maintaining acceptable voltage was implemented. Simulations were conducted using the Multi-objective Differential Evolution Algorithm (MODE). IEEE 33-bus test systems were employed to verify the effectiveness of the proposed method. The results demonstrate that the proposed approach is able to connect larger distributed generators and decrease the economic cost of Distribution Network Operators while maintaining voltage within the statutory limits.

In this paper, a cost-benefit analysis based optimal planning model of battery energy storage system (BESS) in active distribution system (ADS) is established considering a new BESS operation strategy. Reliability improvement benefit of BESS is considered and a numerical calculation method based on expectation is proposed for simple and convenient calculation of system reliability improvement with BESS in planning phase. Decision variables include both configuration variables and operation strategy control variables. In order to prevent the interaction between two types of variables and enhance global search ability, intelligent single particle optimizer (ISPO) is adopted to optimize this model. Case studies on a modified IEEE benchmark system verified the performance of the proposed operation strategy and optimal planning model of BESS.