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Marginal utility functions (MUFs) encapsulate the prosumer willingness to trade energy with other market agents. In large-scale distributed optimization schemes scalability and convergence are crucial, and the assumption of linear MUFs is common. In this work, instead of assuming the shape and coefficients of those functions, a method is proposed to derive them based on the optimization of a prosumer's energy procurement problem. We formulate a rolling-horizon optimization problem that considers asset characteristics and network tariffs, and we utilize forecasts to capture the effects of uncertainty. We use this formulation to calculate the true prosumer MUF. A test case with real data is used to investigate the shape of these functions. Our results reveal that they present a non-linear shape under certain conditions, they cannot be a priori derived, and their form is sensitive to a variety of factors. This indicates that MUFs should be constructed based on an approach similar to the one proposed in this paper to more accurately capture the prosumer's true willingness to trade. A linearized version of these functions, which is computationally attractive and scalable, can be determined using a least-squares estimator that achieves the best fit to the prosumer's preferences under a linearity constraint.

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.

Due to the fast development of distributed energy resources and demand-side response management, agents in electricity markets are becoming more proactive, which boosts the development of peer-to-peer (P2P) market mechanisms. However, to our knowledge, none of the existing works considers clearing both energy and reserve via a P2P market mechanism in order to compensate for the uncertainty originating from renewable generation and allocate the reserve cost induced by uncertainty fairly. In this article, a novel P2P joint energy and reserve market is proposed, where each agent can negotiate with neighboring agents to determine the quantities and prices of traded energy and reserve. We model the renewable generation uncertainty by versatile distribution and determine the required reserve based on a chance-constrained optimization approach. Then, a fully decentralized P2P market based on consensus alternating direction method of multipliers (ADMM) theory is proposed. In addition, to further lower the social cost, we exploit the correlation and complementarity among uncertainties and design a renewable community-based market, where all renewable agents share uncertainty information to community manager for calculating total required reserve. Finally, simulation results show the convergence performance, fairness and scalability of our market mechanism.

Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counter-intuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system.

Current power grid infrastructure was not designed with climate change in mind, and, therefore, its stability, especially at peak demand periods, has been compromised. Furthermore, in light of the current UN’s Intergovernmental Panel on Climate Change reports concerning global warming and the goal of the 2015 Paris climate agreement to constrain global temperature increase to within 1.5–2 °C above pre-industrial levels, urgent sociotechnical measures need to be taken. Together, Smart Microgrid and renewable energy technology have been proposed as a possible solution to help mitigate global warming and grid instability. Within this context, well-managed demand-side flexibility is crucial for efficiently utilising on-site solar energy. To this end, a well-designed dynamic pricing mechanism can organise the actors within such a system to enable the efficient trade of on-site energy, therefore contributing to the decarbonisation and grid security goals alluded to above. However, designing such a mechanism in an economic setting as complex and dynamic as the one above often leads to computationally intractable solutions. To overcome this problem, in this work, we use multi-agent reinforcement learning (MARL) alongside Foundation – an open-source economic simulation framework built by Salesforce Research – to design a dynamic price policy. By incorporating a peer-to-peer (P2P) community of prosumers with heterogeneous demand/supply profiles and battery storage into Foundation, our results from data-driven simulations show that MARL, when compared with a baseline fixed price signal, can learn a dynamic price signal that achieves both a lower community electricity cost, and a higher community self-sufficiency. Furthermore, emergent social–economic behaviours, such as price elasticity, and community coordination leading to high grid feed-in during periods of overall excess photovoltaic (PV) supply and, conversely, high community trading during overall low PV supply, have also been identified. Our proposed approach can be used by practitioners to aid them in designing P2P energy trading markets.

District heating systems become more distributed with the integration of prosumers, including excess heat producers and active consumers. This calls for suitable heat market mechanisms that optimally integrate these actors, while minimizing and allocating operational costs. We argue for the inclusion of network constraints to ensure network feasibility and incentivize loss reductions. We propose a network-aware heat market as a Quadratic Program (QP), which determines the optimal dispatch and a set of nodal marginal prices. While heat network dynamics are generally represented by non-convex constraints, we convexify this formulation by fixing temperature variables and neglecting pumping power. The resulting variable flow heating network model leaves the sign and size of the nodal heat injections flexible, which is important for the integration of prosumers. The market is based on peer-to-peer trades to which we add explicit loss terms. This allows us to trace network losses back to the producer and consumer of these losses. Through a dual analysis we reveal loss components of nodal prices, as well as relations between nodal prices and between seller and buyer prices. A case study illustrates the advantages of the network-aware market by comparison to our proposed loss-agnostic benchmark. We show that the network-aware market mechanism effectively promotes local heat consumption and thereby reduces losses and total cost. We conclude that the proposed loss-aware market mechanism can help reduce operating costs in district heating networks while integrating prosumers. Publication supported by EMB3Rs project.

International audience; We consider the interaction between the distribution grid (physical level) managed by the distribution system operator (DSO), and a financial market in which prosumers optimize their demand, generation, and bilateral trades in order to minimize their costs subject to local constraints and bilateral trading reciprocity coupling constraints. We model the interaction problem between the physical and financial levels as a noncooperative generalized Nash equilibrium problem. We compare two designs of the financial level prosumer market: a centralized design and a peer-to-peer fully distributed design. We prove the Pareto efficiency of the equilibria under homogeneity of the trading cost preferences. In addition, we prove that the pricing structure of our noncooperative game does not permit free-lunch behavior. Finally, in the numerical section we provide additional insights on the efficiency loss with respect to the different levels of agents' flexibility and amount of renewables in the network. We also quantify the impact of the prosumers' pricing on the noncooperative game social cost.

The integration of prosumers (consumers who can both consume and produce energy) in a current district heating network (DHN) brings new challenges to the market and DHN operation, since they can change the thermal flow in the DHN and increase competition in the district heating market. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer (P2P) market to enable bilateral energy trades between producers, prosumers and consumers, coupled with the DHN operation. A Nordic DHN containing prosumers is used to test and validate the proposed methodology. The results point out that the coordination methodology is able to provide compromise solutions between the market negotiation and the DHN operation. An important conclusion is that the coordination methodology encourages prosumer integration in DHN, increasing market competition that may pull down the energy costs for consumers while avoiding DHN’s operating and management burdens. This work is partially supported by the European Union’s Horizon 2020 through the EU Framework Program for Research and Innovation, within the EMB3Rs project under agreement No. 847121.

International audience; We consider a peer-to-peer electricity market, where agents hold private information that they might not want to share. The problem is modeled as a noncooperative communication game, which takes the form of a Generalized Nash Equilibrium Problem, where the agents determine their randomized reports to share with the other market players, while anticipating the form of the peer-to-peer market equilibrium. In the noncooperative game, each agent decides on the deterministic and random parts of the report, such that (a) the distance between the deterministic part of the report and the truthful private information is bounded and (b) the expectation of the privacy loss random variable is bounded. This allows each agent to change her privacy level. We characterize the equilibrium of the game, prove the uniqueness of the Variational Equilibria and provide a closed form expression of the privacy price. In addition, we provide a closed form expression to measure the impact of the privacy preservation caused by inclusion of random noise and deterministic deviation from agents' true values. Numerical illustrations are presented on the 14-bus IEEE network.

Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain’s role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering managers wishing to make sense of blockchain’s potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain’s role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, considering how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain. Sustainable Energy Authority of Ireland (SEAI)

This paper proposes an adaptive segmentation method as a market clearing mechanism for peer-to-peer (P2P) energy trading scheme with large number of market players. In the proposed method, market players participate in the market by announcing their bids. In the first step, players are assigned to different segments based on their features, where the balanced k-means clustering method is implemented to form segments. These segments are formed based on the similarity between players, where the amount of energy for trade and its corresponding price are considered as features of players. In the next step, a distributed method is employed to clear the market in each segment without any need to private information of players. The novelty of this paper relies on developing an adaptive algorithm for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads. The proposed approach can be used along with any distributed method for market clearing. In this paper, two different structures including community-based market and decentralized bilateral trading market are used to demonstrate the efficacy of the proposed method. Simulation results show the beneficial properties of the proposed segmentation method.

How are prices and market outcomes determined on peer-to-peer platforms? More importantly, how should we expect price-setting and demand behavior to change as these markets mature? We provide the first empirical analysis of the world’s leading carsharing platform, BlaBlaCar. Our econometric model explicitly accounts for the joint determination of price and quantity demanded and finds that pricing decisions evolve as drivers gain experience with the platform. More-experienced drivers set lower prices and, controlling for price, sell more seats. Our interpretation is that more-experienced drivers on BlaBlaCar learn to lower their prices as they gain experience. Further, we find that driver demographics matter. The demographic characteristic with the quantitatively largest effect is for drivers with an Arabic-sounding name, for whom there is meaningfully lower demand, despite the fact that these drivers set lower prices. In total, our results suggest that peer-to-peer markets such as BlaBlaCar share some characteristics with other types of peer-to-peer markets such as eBay but remain a unique and rich setting in which there are many new insights to be gained.

Arguably a cornerstone of credit risk modelling is the probability of default. This article aims is to search for the evidence of relationship between loan characteristics and probability of default on peer-to-peer (P2P) market. In line with that, two loan characteristics are analysed: 1) loan term length and 2) loan purpose. The analysis is conducted using survival analysis approach within the vintage framework. Firstly, 12 months probability of default through the cycle is used to compare riskiness of analysed loan characteristics. Secondly, log-rank test is employed in order to compare complete survival period of cohorts. Findings of the paper suggest that there is clear evidence of relationship between analysed loan characteristics and probability of default. Longer term loans are more risky than the shorter term ones and the least risky loans are those used for credit card payoff.