Virtual power producers integration into MASCEM

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. Under this context distributed generators, owned by different decentralized players can provide a significant amount of the electricity generation. To get negotiation power and advantages of scale economy, these players can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multi-technology and multi-site heterogeneous entities. Virtual Power Producers should adopt organization and management methodologies so that they can make Distributed Generation a really profitable activity, able to participate in the market. In this paper we address the integration of Virtual Power Producers into an electricity market simulator –MASCEM – as a coalition of distributed producers

Integration of Pumping in Virtual Power Players management considering demand response

The increase of distributed generation in several countries around the world brought several challenges to planning and operation of the electric network. In situations of high penetration of non-storable resources generation, mainly wind power, demand response programs and pumping may be applied, in order to encourage the increase of consumption, to guarantee a better electric network management. The present paper presents a methodology focusing on demand response programs, distributed generation and pumping, which aims to be used by a Virtual Power Player, who is able to manage the available resources optimizing its costs. The main objective is to use pump to store water to reservoirs, so the reservoir owner would have enough power to boost for energy generation. The case study includes 2223 consumers and 47 distributed generators, which was implemented using a Portuguese power system real scenario, 9th March 2014.The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Multi-agent systems and birtual producers in electronic marketplaces

This paper presents an agent-based simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms, considering user risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. In the simulated market agents interact in several different ways and may joint together to form coalitions. In this paper we address multi-agent coalitions to analyse Distributed Generation in Electricity Market

A Self-Tuning procedure for resource management in InterCloud Computing

Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, College of Computer Science, North China University of Technology, Beijing, China The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.InterCloud Computing is a new cloud paradigm designed to guarantee service quality or performance and availability of on-demand resources. InterCloud enables cloud interoperability by promoting the interworking of cloud systems from different cloud providers using standard interfacing. Resource management in InterCloud, considered as an important functional requirement, has not attracted commensurate research attention. The focus of this paper is to propose a Software Cybernetic approach, in the form of an adaptive control framework, for efficient management of shared resources in peer-to-peer InterCloud computing. This research effort adopts cooperative game theory to model resource management in InterCloud. The space of cooperative arrangements (resource sharing) between the participant cloud systems is presented by using Integer Partitioning to characterise the worst case communication complexity in peer to peer InterCloud. Essentially, this paper presents an Integer partition based anytime algorithm as an optimal cost solution to the bi-objective optimisation problem in resource management, anchored principally on practical trade-off between the desired performance (quality of service) and communication complexity of collaborating resource clouds

Algorithms for Graph-Constrained Coalition Formation in the Real World

Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called $m+a$ functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).Comment: Accepted for publication, cite as "in press

Coalition structure generation over graphs

We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) → R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components values. This problem is generally NP-complete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected members — that is, two nodes have no effect on each others marginal contribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NP-complete for planar graphs, and hence, for any Kk minor free graphs where k ≥ 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph

Contextual and Possibilistic Reasoning for Coalition Formation

In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics

Agent-Organized Network Coalition Formation

This thesis presents work based on modeling multi-agent coalition formation in an agent organized network. Agents choose which agents to connect with in the network. Tasks are periodically introduced into the network. Each task is defined by a set of skills that agents must fill. Agents form a coalition to complete a task by either joining an existing coalition a network neighbor belongs to, or by proposing a new coalition for a task no agents have proposed a coalition for. We introduce task patience and strategic task selection and show that they improve the number of successful coalitions agents form. We also introduce new methods of choosing agents to connect to in the network and compare the performance of these and existing methods