Electricity consumption is highly correlated with the level of human development, which alongside electrification is expected to significantly increase global demand for electricity in the coming decades. In current electricity systems, most of the electricity is generated by large fossil-fuel power plants on-demand and it is distributed by centrally-managed electricity grids. The increasing demand for electricity, however, should not go hand in hand with the simultaneous intensification of fossil-fuel mine and use, which is a driving cause of rising average temperatures on Earth’s surface. Natural sources such as the sun and wind are expected to replace conventional sources of electricity, such as coal and gas power plants, in the near future, providing a key measure to address climate change and abate the effects of global warming. However, the intermittent and distributed nature of renewable electricity sources requires a redesign of conventional electricity grids that were originally designed following a top-down approach.@en

We study mechanisms to incentivize demand response in smart energy systems. We assume agents that can respond (reduce their demand) with some probability if they prepare prior to the real-ization of the demand. Both preparation and response incur costs to agents. Previous work studies truthful mechanisms that select a minimal set of agents to prepare and respond such that a fixed demand reduction target is achieved with high probability. In this work we additionally consider the balancing responsibility of a retailer under a given demand forecast and imbalance price: The retailer is responsible to purchase additional reserve capacity at a high imbalance price to cover any excess in the demand. In this extended setting we study mechanisms that request only a subset of prepared agents to respond since the reduction target depends on the realization of the demand: We propose: (i) a sequential mechanism that in each round embeds a second-price auction and is truthful under some mild assumptions for the setting, and (ii) a truthful combinatorial mechanism that runs in polynomial time and uses VCG payments. We show that both mechanisms guarantee non-negative utility in expectation for both agents and the retailer (mechanism), and can further be used for simultaneous downward and upward flexibility. Last, we verify our theoretical findings in an empirical evaluation over a wide range of mechanism parameters.

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The imperfect decision-making of human buyers participating in retail markets varies from fundamental models that assume rational economic choices: even in markets with identical items human buyers are not rational, i.e., buyers do not always choose the cheapest option. Recent developments in artificial intelligence and e-commerce enable market participation by software agents that are (almost) perfectly rational due to their computational capacity. However, the increasing degree of buyers’ rationality might have unfavorable effects on retail markets with regards to the competition between sellers and the resulting prices. In this paper, we study the effects of varying degrees of buyers’ rationality on the competition and the prices buyers face in retail markets with identical items. We use the multinomial logit function to model different degrees of buyers’ rationality. We further model the competition between sellers using k-level reasoning: each seller computes the price to offer (best response strategy) with regards to its belief for the competition. First, we derive an analytical best response strategy (price) of a seller given the competing prices and the degree of buyers’ rationality, and show that there exists an optimal degree of buyers’ rationality that minimizes the price. Last, we use evolutionary game theory to show that perfect rationality leads to unstable competition dynamics increasing the overall cost for buyers. In contrast, bounded rationality leads to smoother dynamics and lower cost for buyers. Our insights raise the need to revisit design objectives for software agents in retail markets in light of their wider systematic impact.

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Current electricity tariffs for retail rarely provideincentives for intelligent demand response of flexiblecustomers. Such customers could otherwisecontribute to balancing supply and demand in futuresmart grids. This paper proposes an innovativerisk-sharing tariff to incentivize intelligentcustomer behavior. A two-step parameterized paymentscheme is proposed, consisting of a prepaymentbased on the expected consumption, and asupplementary payment for any observed deviationfrom the anticipated consumption. Within a gametheoreticalanalysis, we capture the strategic con-flict of interest between a retailer and a customerin a two-player game, and we present optimal, i.e.,best response, strategies for both players in thisgame. We show analytically that the proposed tariffprovides customers of varying flexibility with variableincentives to assume and alleviate a fraction ofthe balancing risk, contributing in this way to theuncertainty reduction in the envisioned smart-grid.@en