Fairness in smart grid congestion management

With the energy transition, grid congestion is increasingly becoming a problem. This paper proposes the implementation of fairness in congestion management by presenting quantitative fair optimization goals and fairness measuring tools. Furthermore, this paper presents a congestion management solution in the form of an egalitarian allocation mechanism. Finally, this paper proves that this mechanism is truthful, pareto efficient, and maximizes a fair optimization goal

Bundling and pricing for information brokerage: customer satisfaction as a means to profit optimization

Traditionally, the study of on-line dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. In this paper we discuss the relatively overlooked potential of these strategies to on-line learn more about customers' preferences. Based on this enhanced customer knowledge an information broker can-- by tailoring the brokerage services more to the demand of the various customer groups-- persuade customers to engage in repeated transactions (i.e., generate customer lock-in). To illustrate the discussion, we show by means of a basic consumer model how, with the use of on-line dynamic bundling and pricing algorithms, customer lock-in can occur. The lock-in occurs because the algorithms can both find appropriate prices and (from the customers' perspective) the most interesting bundles. In the conducted computer experiments we use an advanced genetic algorithm with a niching method to learn the most interesting bundles efficiently and effectively

Dynamic routing problems with fruitful regions: models and evolutionary computation

We introduce the concept of fruitful regions in a dynamic routing context: regions that have a high potential of generating loads to be transported. The objective is to maximise the number of loads transported, while keeping to capacity and time constraints. Loads arrive while the problem is being solved, which makes it a real-time routing problem. The solver is a self-adaptive evolutionary algorithm that ensures feasible solutions at all times. We investigate under what conditions the exploration of fruitful regions improves the effectiveness of the evolutionary algorith

Co-evolving automata negotiate with a variety of opponents

Real-life negotiations typically involve multiple parties with different preferences for the different issues and bargaining strategies which change over time. Such a dynamic environment (with imperfect information) is addressed in this paper with a multi-population evolutionary algorithm (EA). Each population represents an evolving collection of bargaining strategies in our setup. The bargaining strategies are represented by a special kind of finite automata, which require only two transitions per state. We show that such automata (with a limited complexity) are a suitable choice in a computational setting. We furthermore describe an EA which generates highly-efficient bargaining automata in the course of time. A series of computational experiments shows that co-evolving automata are able to discriminate successfully between different opponents, although they receive no explicit information about the identity or preferences of their opponents. These results are important for the further development of evolving automata for real-life (agent system) applications

Why agents for automated negotiations should be adaptive

We show that adaptive agents on the Internet can learn to exploit bidding agents who use a (limited) number of fixed strategies. These learning agents can be generated by adapting a special kind of finite automata with evolutionary algorithms (EAs). Our approach is especially powerful if the adaptive agent participates in frequently occurring micro-transactions, where there is sufficient opportunity for the agent to learn online from past negotiations. More in general, results presented in this paper provide a solid basis for the further development of adaptive agents for Internet applications

Why agents for automated negotiations should be adaptive

We show that adaptive agents on the Internet can learn to exploit bidding agents who use a (limited) number of fixed strategies. These learning agents can be generated by adapting a special kind of finite automata with evolutionary algorithms (EAs). Our approach is especially powerful if the adaptive agent participates in frequently occurring micro-transactions, where there is sufficient opportunity for the agent to learn online from past negotiations. More in general, results presented in this paper provide a solid basis for the further development of adaptive agents for Internet applications

An electricity market with fast bidding, planning and balancing in smart grids

In future energy systems, peaks in the daily electricity generation and consumption are expected to increase. The "smart grid" concept aims to maintain high levels of efficiency in the energy system by establishing distributed intelligence. Software agents (operating on devices with unknown computational capabilities) can implement dynamic and autonomous decision making about energy usage and generation, e.g. in domestic households, farms or offices. To reach satisfactory levels of efficiency and reliability, it is crucial to include planning-ahead of the energy-involving activities. Market mechanisms are a promising approach for large-scale coordination problems about energy supply and demand, but existing electricity markets either do not involve planning-ahead sufficiently or require a high level of sophistication and computing power from participants, which is not suitable for smart grid settings. This paper proposes a new market mechanism for smart grids, ABEM (Ahead- and Balancing Energy Market). ABEM performs an ahead market and a last-minute balancing market, where planning-ahead in the ahead market supports both binding ahead-commitments and reserve capacities in bids (which can be submitted as price functions). These features of planning-ahead reflect the features in modern wholesale electricity markets. However, constructing bids in ABEM is straightforward and fast. We also provide a model of a market with the features mentioned above, which a strategic agent can use to construct a bid (e.g. in ABEM), using a decision-theoretic approach. We evaluate ABEM experimentally in various stochastic scenarios and show favourable outcomes in comparison with a benchmark mechanism