Coalitional energy purchasing in the smart grid

Abstract

Matching demand and supply is recognized as a crucial issue for smart grids, and ICT-based solutions are essential to deliver the infrastructure, algorithms and mechanisms for demand-supply balancing. To date, most work in this area focus on providing users with real time feedback on energy prices and consumption, or on load scheduling of home appliances for individual user consumption. In this paper, we take a complementary approach by exploiting social relationships among consumers to organise them into coalitions of Virtual Electricity Consumers (VECs) that buy electricity as a single customer in order to get a discount on electricity through collective buying. Specifically, we model our problem as a coalitional game and provide an algorithm, based on linear programming, to form VECs. The VECs formed by our algorithm are both efficient (i.e., minimizing the sum of users' payments) and stable (i.e., no user has any incentive to break away). We empirically analyse our approach using real consumption data for a set of households located in UK. Our analysis provides interesting insights into the relationship between structure and stability of VEC's and prices within the electricity market.

When Push Comes to Shove: Compensating and Opportunistic Strategies in a Collective-Risk Household Energy Dilemma

“…This is not accounted for in the policy documents, which focus on the positive outputs of a community purchasing initiative. Researchers have shown that near-future changes in energy infrastructure, e.g., forthcoming smart meter rollouts in the UK, will make it easier to identify which consumers might benefit by forming collectives (Vinyals et al, 2012) and what the tools might look like that help collectives deal with energy retailers (Ramchurn et al, 2013). However, in reality communities can be transient and marginalizing (Harvey and Braun, 1999), particularly to those not predicated toward collective action (Hoffman and High-Pippert, 2010), and reactions to energy initiatives by different communities will not necessarily remain positive (Walker et al, 2010).…”

Determinants of Smart Energy Demand Management: An Exploratory Analysis

“…and for energy demand management (Source: Authors) ision, collaborative arrangements in the energy context he expected level of sustainability and the form of inte rated consideration is highlighted in Table 1 Whereas most work related in this perspective overlooks the comprehensive analysis and hence partially comments these dimensions in collective way as a peak load saving strategies. Such as [19][20] focus only on the collaboration as a collective energy purchasing by overlooking the consumers proactive participation and other determinants such as behavioral factors in creating the demand flexibility under energy sharing perspective. …”

A Tutorial on Optimization for Multi-Agent Systems

“…Thus, News Ltd. would run a single auction this time. In the bid call, both W 1 and W 2 are requested to communicate the cost of servicing each possible subset of R: (1) to do no delivery at all; (2) to deliver only to A; (3) to deliver only to B; and (4) to deliver to both A and B. In this case, the winner determination problem faced by the auctioneer is a combinatorial optimization problem: choosing which bidder to award to each service so as to minimize the overall total cost.…”

“…Need to engage in a negotiation process that will allow them to align their individual objectives (e.g., through peer to peer messaging or through receiving commands from a centre). Such a negotiation process may be particularly important when the actors in the system may be self-interested and therefore require some form of payment or nonmonetary reward (e.g., participants in a groupbuying programme [1,2] or collectives of wind generators where each generator belongs to a dierent stakeholder [3]). In these cases, the process is often termed a`coalitional game' given the strategic decisions each stakeholder may make to join dierent collectives or coalitions.…”

“…Finally, Section 5 summarises and relates the multi-agent optimisation problems visited in this tutorial. 2.…”