Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture

In this paper, a comprehensive real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed. A hierarchical bi-level control scheme (BLCS) with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists of sub-components of load demand prediction, renewable generation resource integration, electrical power-load balancing, and responsive load demand. In the primary level, EMSs are operating separately for each microgrid (MG) by considering the problem constraints, power set-points of generation resources, and possible shortage or surplus of power generation in the MGs. In the proposed framework, minimum information exchange is required among MGs and the distribution system operator. It is a highly desirable feature in future distributed EMS. Various parameters such as load demand and renewable power generation are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi's orthogonal array testing approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS in the secondary level. In order to validate the proposed control structure, a test system is simulated and optimized based on multiperiod imperialist competition algorithm. The obtained results clearly show that the proposed BLCS is effective in achieving optimal dispatch of generation resources in systems with multiple MGs

Multi-time scale control of demand flexibility in smart distribution networks

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control

Exploiting Traffic Balancing and Multicast Efficiency in Distributed Video-on-Demand Architectures

Distributed Video-on-Demand (DVoD) systems are proposed as a solution to the limited streaming capacity and null scalability of centralized systems. In a previous work, we proposed a fully distributed large-scale VoD architecture, called Double P-Tree, which has shown itself to be a good approach to the design of flexible and scalable DVoD systems. In this paper, we present relevant design aspects related to video mapping and traffic balancing in order to improve Double P-Tree architecture performance. Our simulation results demonstrate that these techniques yield a more efficient system and considerably increase its streaming capacity. The results also show the crucial importance of topology connectivity in improving multicasting performance in DVoD systems. Finally, a comparison among several DVoD architectures was performed using simulation, and the results show that the Double P-Tree architecture incorporating mapping and load balancing policies outperforms similar DVoD architectures.This work was supported by the MCyT-Spain under contract TIC 2001-2592 and partially supported by the Generalitat de Catalunya- Grup de Recerca Consolidat 2001SGR-00218

Steering the Smart Grid

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies and optimization methodologies were developed to increase the efficiency, maintain the grid stability and support large scale introduction of renewable sources. In previous work, we showed the effectiveness of our three-step methodology to reach these objectives, consisting of 1) offline prediction, 2) offline planning and 3) online scheduling in combination with MPC. In this paper we analyse the best structure for distributing the steering signals in the third step. Simulations show that pricing signals work as good as on/off signals, but pricing signals are more general. Individual pricing signals per house perform better with small prediction errors while one global steering signal for a group of houses performs better when the prediction errors are larger. The best hierarchical structure is to use consumption patterns on all levels except the lowest level and deduct the pricing signals in the lowest node of the tree

Smart microgrids and virtual power plants in a hierarchical control structure

In order to achieve a coordinated integration of distributed energy resources in the electrical network, an aggregation of these resources is required. Microgrids and virtual power plants (VPPs) address this issue. Opposed to VPPs, microgrids have the functionality of islanding, for which specific control strategies have been developed. These control strategies are classified under the primary control strategies. Microgrid secondary control deals with other aspects such as resource allocation, economic optimization and voltage profile improvements. When focussing on the control-aspects of DER, VPP coordination is similar with the microgrid secondary control strategy, and thus, operates at a slower time frame as compared to the primary control and can take full advantage of the available communication provided by the overlaying smart grid. Therefore, the feasibility of the microgrid secondary control for application in VPPs is discussed in this paper. A hierarchical control structure is presented in which, firstly, smart microgrids deal with local issues in a primary and secondary control. Secondly, these microgrids are aggregated in a VPP that enables the tertiary control, forming the link with the electricity markets and dealing with issues on a larger scale

Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing

In this paper we propose a two-stage protocol for resource management in a hierarchically organized cloud. The first stage exploits spatial locality for the formation of coalitions of supply agents; the second stage, a combinatorial auction, is based on a modified proxy-based clock algorithm and has two phases, a clock phase and a proxy phase. The clock phase supports price discovery; in the second phase a proxy conducts multiple rounds of a combinatorial auction for the package of services requested by each client. The protocol strikes a balance between low-cost services for cloud clients and a decent profit for the service providers. We also report the results of an empirical investigation of the combinatorial auction stage of the protocol.Comment: 14 page