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Bilateral ratings on online marketplaces may soften price competition and sellers of higher quality may charge lower prices. Abstract. Peer-to-peer (P2P) markets have become a critical aspect of the modern economy. We consider a P2P market in which a time-sensitive service is provided through a platform that matches providers of varying qualities to customers of varying costs. The P2P platform features bilateral ratings, which distinguish it from a traditional market: ratings of a provider reveal the provider's service quality and ratings of a customer reveal the customer's service cost. The existence of a cost measure in the P2P market leads to novel pricing considerations: a provider can attract low-cost customers by charging a low price, leading to an endogenous composition effect. As a result, equilibrium prices may decrease as customers become more costly to serve or as the platform's commission rate gets higher. Under certain conditions, high-quality providers may even charge a lower equilibrium price than low-quality providers in order to cherry-pick low-cost customers. Exploratory analysis reveals that, compared with unilateral ratings, bilateral ratings may soften provider competition and raise equilibrium prices as the providers target customers in different cost segments. History: Anthony Dukes served as the senior editor. Funding: This work was supported by the NET Institute (summer research fund). Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2022.0158. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to effectively manage distributed energy resources (DERs) by facilitating peer-to-peer (P2P) energy trading among smart homes. This system incentivizes peers to trade surplus energy, thereby maximizing social welfare. The proposed day-ahead demand-side management (DSM) technique considers load prioritization based on individual user needs. It employs an optimal energy allocation policy that encourages peers to purchase energy at reduced rates while selling it at advantageous prices. This strategy optimizes the allocation of traded energy to higher-priority loads, followed by lower-priority ones, all while considering user preferences. The proposed method results in an average reduction in energy costs from 14.80 % to 2.70 % for all consumers and an average increase in revenues from 17.35 % to 9.07 % for all prosumers compared to the grid and previously proposed studies. Moreover, the proposed approach significantly enhances the consumer satisfaction Index (SI), with a 46.8 % increase over the previous research study. Simulation results demonstrate substantial economic benefits for all market participants, improved SI, enhanced community self-sufficiency, and reduced dependence on the main grid. The proposed methodology offers a practical and fair solution for managing DERs through P2P energy trading by implementing load prioritization within smart homes. It highlights the potential to establish a sustainable and efficient energy ecosystem, thereby enhancing the effective utilization of surplus energy from DERs.

Integration of prosumers in district heating networks brings new challenges to the market and the network operation since they can change the thermal flow and increase competition. Thus, it is mandatory to develop new market structures and network management mechanisms. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer market without a supervising entity. The goal is to achieve higher revenue by coping with the requirements of each agent. Furthermore, the model is validated through network nodal analysis inspired by the power sector. The results in a Nordic network point out that the coordination methodology can provide compromise solutions between market negotiation and network operation. This methodology succeeded in providing reliable network solutions, fixing 99.88% of network burdens just after one iteration, and encouraging prosumers' integration. This increases market competition which lowers the energy costs for consumers while avoiding the network's operating burdens. [ABSTRACT FROM AUTHOR]

The paper develops a new dynamic choice modeling framework and applies it to examine owner decisions in P2P sharing markets. Peer-to-peer (P2P) sharing marketplaces enable sharing of idle resources. When a renter requests an owner's resource, the owner needs to decide whether to accept the request: accepting it helps the owner fill up the idle periods of the resource and generate a payoff but reduces the flexibility to serve a future request for a longer duration. This paper develops a framework to uncover the tradeoffs faced by owners on these platforms when making acceptance decisions, which can be used by owners to optimize their decisions and by platforms to improve their operations. The model explicitly accommodates two types of owners: some are attentive to the availability states of their cars and forward-looking, whereas others myopically make the acceptance decisions. Applying the model to unique data from a leading peer-to-peer car sharing platform in China, we obtain similar sizes of both types of owners and find that female, experienced, and younger owners are more likely to be strategic. The results also reveal the differentiated preferences of the two types of owners toward their renters. Building on model estimates, we calibrate the option value of each day in the future (i.e., the value of having the day available) for strategic owners and find it to first increase, then decrease. Two counterfactual analyses are conducted. The first analysis shows that if the platform imposes a minimum rental duration, strategic owners may become more reluctant to accept requests, even if the current availability state entails a higher expected payoff. The second analysis shows that with better understanding of its owners, the platform can greatly improve the matching efficiency by optimal (re)allocation of rental requests, a move that benefits almost all participants in the business. History: K. Sudhir served as the senior editor for this article. Funding: This work was supported by the Hong Kong Polytechnic University [Grant A0038957] and the National University of Singapore [Research Grant R-316-000-104-133]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mksc.2022.1369. [ABSTRACT FROM AUTHOR]

Meeting electricity demand by the generation of electricity from locally distributed energy sources has gained much success over the years. Among different frameworks for such renewable generation and consumption, peer-to-peer (P2P) markets have proved to be an efficient solution. As the pricing mechanism is an integral part of P2P markets, optimal price determination for electricity trading that ensures the profitability of the participants is the key to success in such markets. In addition to profitability, the pricing mechanism should be able to incorporate users’ reserved prices and grid supply uncertainty to be implementable in developing countries. To achieve this objective and based on participants’ preferences, an effective game-theoretic model is proposed to formulate the trading pairs among consumers and sellers. Then, keeping in view the participants’ reserved prices for electricity trading, an effective and novel method based on the game-theoretic approach is proposed to determine the electricity price in the direct P2P electricity market. The proposed model is evaluated on a market having 22 participants. Among these, 11 participants act as electricity consumers, and the other 11 act as sellers. Simulation results show that the proposed algorithm is more effective as it further reduces the electricity bills for consumers from 5% to 8% and increases the revenues of sellers from 13% to 15% as compared to other proposed mid-range auction and uniform pricing models.

To realise the clean energy transition, peer-to-peer (P2P) renewable energy sharing markets have been proposed as one possible solution for achieving such a goal and are recognised as a potential path to achieving other goals such as affordable and reliable energy. Existing studies have shown that coordination at the micro level can be achieved by employing such P2P market structures. A pressing question concerns how to set the trade price such that the community coordinates in a way that maximises social welfare. A solution to this question based on multi-agent reinforcement learning (MARL) has been provided as a proof-of-concept in a single environment. However, various factors such as climate and community scale have been shown to affect the collective performance in such energy-sharing communities. In this work, to test the wider applicability of the proposed solution, a full factorial experiment based on the factors of climate , community scale , and price mechanism , is conducted to ascertain the response of the community w.r.t. the outputs: community self-sufficiency , total net-loss , and income equality. In short, we find that a community stands an odds of 2 to 1 in higher savings by adopting a smart agent. • Multi-agent reinforcement learning has been used for optimising energy trading. • A prosumer community is expected to fare better under the proposed smart agent. • A community stands an odds of 2 to 1 in higher savings by adopting a smart agent. • Income equality can be disregarded in larger prosumer communities. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

Distributed energy resources, together with information and communication technologies, have transformed the traditional electricity consumers into proactive consumers, namely prosumers. Prosumers can exchange their surplus energy with consumers through peer-to-peer (P2P) energy sharing. In this paper, the framework of P2P energy exchange in active distribution networks is developed using a multi-round double auction (MRDA) with average pricing mechanism (APM) integrated with distributional locational marginal price. The advantages of the proposed P2P framework include, 1) modeling and integration of the costs of voltage regulation, congestion, and power loss into the payments of agents for each transaction; 2) the entire distribution network clustered into multiple zones with transactions cleared at different levels, which decreases the additional costs for successful transactions, reduces the computational time, and increases the number of successful transactions; and 3) the matching algorithm encourages more prosumers and consumers to participate in P2P energy sharing and increases the efficiency and benefit from P2P market. The proposed MRDA-APM framework is validated by testing on the 33-node and 141-node distribution test systems. Simulation results demonstrate the effectiveness of the proposed mechanism for P2P energy exchange from both technical and computational viewpoints. [ABSTRACT FROM AUTHOR]

• A P2P energy and flexibility market in the integrated energy system is proposed. • The operational conditions of the integrated networks are taken into account. • A novel duality-based methodology is proposed for loss allocation purposes. • A validation analysis is developed to guarantee the deployment of flexibilities. This paper introduces a new day-ahead peer-to-peer (P2P) trading mechanism for Integrated Electricity-Heat Networks (IEHNs), which enables agents to negotiate and trade heat and electricity. The mechanism also includes a flexibility service that utilizes rapid capacities from flexible resources, such as fast dispatchable agents, electrical and heat loads, and energy hubs. This service allows renewable energy participants to address unforeseen generation shortfalls or surpluses in real-time. To ensure that traded flexibilities can be delivered within real-time operational constraints, the proposed day-ahead operation model is enhanced with flexibility deployment scenarios. The IEHN operator, as a non-profit market participant, collaborates with other agents to ensure the secure operation of the IEHN. Additionally, a duality-based approach is employed to accurately determine the proportion of transactions and the operational constraints in energy losses of the IEHN. The proposed market clearing mechanism is tested in a 16-agent P2P market located on a 33-bus power distribution system coupled with a 10-node district heating system. Numerical results demonstrate the accuracy and efficiency of the proposed market mechanism in pricing, loss allocation, and the feasibility of flexibility transactions. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of active consumers in the distribution networks transforms the modern power markets’ structure more independent, flexible, and distributed. Specifically, in the recent trend of peer-to-peer (P2P) transactive energy systems, the traditional consumers became prosumers (producer+consumer) who can maximize their energy utilization by sharing it with neighbors without any conventional arbitrator in the transactions. Although a distributed energy pricing scheme is inevitable in such systems to make optimal decisions, it is challenging to establish under the influence of non-linear physical network constraints with limited information. Therefore, this paper presents a distributed pricing strategy for P2P transactive energy systems considering voltage and line congestion management, which can be utilized in various power network topologies. This paper also introduces a new mutual reputation index as a product differentiation between the prosumers to consider their bilateral trading willingness. In this paper, a Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm is realized instead of the standard ADMM algorithm to improve the convergence rate. The effectiveness of the proposed approach is validated through software simulations. The result shows that the algorithm is scalable, converges faster, facilitates easy implementation, and ensures maximum social welfare/profit. [ABSTRACT FROM AUTHOR]

Ride-hailing services (e.g., Uber, Lyft) have drawn attention as a disruptive innovation in the tourism industry as they provide a new option for transport while on vacation or a business trip. Few studies have examined how travellers perceive the value of this new mode of transportation and modelled their intent to use ride-hailing services. Millennial consumers are known for their early adoption of smart technologies with different supply chains and portable internet devices (e.g., cell phones). This research examines the impact of perceived value on millennial travellers' intentions to use ride-hailing services in two rapidly changing tourism economies embracing smart phone access to transport services. Primary data were collected with millennials located in urban universities in the US and China. Data were analysed using ordinary least squares estimates. The results revealed that price and relational value positively influenced millennial travellers' intentions to use ride-hailing services in both samples. These influences remained significant after controlling for previous experiences with mobile technology, perceived safety, and regulations for ride-hailing services. The two millennial samples, however, exhibited different consumer factor influences. While quality and perceived regulations for ride-hailing services predicted millennial travellers' use intentions in the US sample, previous experience of using mobile technology influenced travellers' use intentions in the Chinese sample. As companies like Uber and Lyft expand and new providers enter the market, consumer behaviour research on perceived value can inform how business models might differ across countries and services should be tailored for each destination. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

• First study on online taxi platforms with two systems of operation for drivers. • As for e.g. Didi Chuxing in real life, drivers opt to either be informed about rides or directly assigned. • We derive the maximum matching radius and optimal allocation of rides over the Inform and Assign systems. • Empirical results show that only drivers relatively close to a customer should be informed, and generally a relatively small fraction of rides should be allocated to the Inform system. • The platform should not always allocate more requests to the Inform system if destination selection becomes more important for drivers. 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. [ABSTRACT FROM AUTHOR]

A novel peer-to-peer (P2P) market design is proposed in this work for the distribution grid level. Envisioning that the grid constraints violations are the major challenge for P2P energy sharing, we propose these to be handled through the ancillary service (AS) market. By calculating the decomposable distribution locational marginal prices (DLMPs), the essential price signals of procuring ASs can be recovered to determine the grid usage prices (GUPs) to each P2P transaction. Hence, the GUPs, due to their decomposable properties, act as incentive signals for the P2P market to support the grid operation in terms of loss reduction, voltage support and congestion management. The proposed market design comprises i) an interactive market design of P2P trade & AS and, ii) a fully distributed peer-centric market-clearing model for P2P energy trade. The duality analysis provides the composition of market equilibrium prices of P2P trading and their interpretations. The case studies demonstrate the effectiveness of the proposed P2P trade to support grid operational objectives. [ABSTRACT FROM AUTHOR]

Tradable credit schemes (TCS) have been receiving increasing attention as an alternative to congestion pricing due to considerations of equity and revenue neutrality. Although it is typically assumed that credit transactions occur between travelers directly, i.e., via peer-to-peer (P2P) trading, the underlying mechanism that achieves market clearing (in terms of matching of sellers and buyers and pricing of credits) has not been studied in sufficient detail. This study extends the current literature on TCS by proposing two types of P2P trading paradigms that define the rules of matching selling and buying orders, market price adjustment, and the individual bidding format. Together with a peer-to-regulator (P2R) design, all trading paradigms are tested in the context of the morning commute problem under a given distance-based time-of-day credit tariff scheme. Numerical results demonstrate that the proposed P2P trading paradigms – in the absence of transaction costs – lead to a near identical equilibrium in terms of social welfare gains, departure flows, and credit price as that obtained from P2R schemes. Further, the P2P trading mechanisms ensure budget neutrality of credits as well as revenue neutrality of the regulator during the day-to-day process. • Implementations of tradable credit schemes with P2P trading mechanisms. • No welfare loss incurred under proposed P2P paradigms. • Guaranteed budget neutrality of credits and revenue neutrality under P2P paradigms. [ABSTRACT FROM AUTHOR]

In recent years, the use of distributed energy resources has accelerated to deal with global warming. As these resources are decentralized, the current centralized energy market must be modified to accommodate their characteristics. Advances in information technology have made it possible to implement peer-to-peer (P2P) markets. However, completely removing the distribution system operator from P2P exchanges can risk the network's security. Conversely, establishing network security only by the operator can decline the independence of agents. This paper proposes a decentralized framework for implementing the P2P market based on the alternating direction method of multipliers, which maintains the distribution system's constraints, considering the agents' independent nature. The agents reach convergence with minimal exchange of information while maintaining the network constraints, including voltage and current limits. The network constraints are included using a proposed sensitivity approach in the sub-problem of each agent. In this approach, by calculating the sensitivity coefficients of voltage and current, the network security can be evaluated with the changes of active and reactive powers by agents. The simulation results demonstrate that the proposed framework can efficiently maintain network constraints. The short running time of the P2P trading with the proposed framework makes it feasible for practical applications. • Developing a new P2P trading framework under distribution network constraints. • Preserving the independent nature of P2P participants. • The alternating direction method of multipliers is applied in the framework. • Applying the network constraints using a sensitivity approach in the sub-problems. • The studies prove the efficacy of the framework in maintaining network constraints. [ABSTRACT FROM AUTHOR]

Scholars have long recognized status and reputation as pervasive forces reproducing comparative advantage in social and economic systems. Yet, due in part to methodological challenges, relatively few studies have examined how status and reputation interact. We use data from an online market for peer-to-peer lending to study independent and joint effects of status and reputation on borrowers’ success at obtaining loans. First, we find a positive main effect of status, even when reputational signals are reliable and abundant. Second, we find that status matters the most for borrowers with moderate (rather than high or low) reputations, suggesting a curvilinear effect of status x reputation on loans. These results support the idea that status matters not only under conditions of too little information that creates information asymmetry, as typically assumed, but also under conditions of abundant information and too many choices that creates ambiguity about how to evaluate candidates. [ABSTRACT FROM AUTHOR]

• A joint energy-reserve prosumer-centric market is proposed. • The trading mechanism not only enables peer-to-peer (P2P) energy trading but also quantifies the reserve cost and the value of flexibility. • A blockchain-based trading algorithm is proposed to implement an autonomous and trustworthy prosumer-centric market. • A pipelined delegated Byzantine fault tolerance (PDBFT) consensus algorithm is proposed to improve the efficiency of blockchain-based energy trading. With the increasing penetration of distributed energy resources, the traditional producer-centric electricity market is moving to a prosumer-centric market, where prosumers can trade with each other in an autonomous pattern. However, there remain research gaps on the pricing and allocation of joint energy and reserves in an autonomous prosumer-centric market. This paper firstly designs a joint energy-reserve peer-to-peer (P2P) trading mechanism. The mechanism not only enables P2P energy trading but also quantifies the reserve cost and the value of flexibility. Then, a blockchain-based trading algorithm is proposed to implement a trustworthy prosumer-centric market. A pipelined delegated Byzantine fault tolerance (PDBFT) consensus algorithm is proposed to ensure robustness and improve the efficiency of the autonomous trading process. Numerical results show the effectiveness of the trading mechanism and the performance of the blockchain-based trading algorithm. Compared with only considering energy trading, the proposed mechanism reduces the total cost of the market by 16.03%. Compared with using the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, the computational time of market clearing on blockchain is decreased by 45.90%. [ABSTRACT FROM AUTHOR]

• Overview of full peer-to-peer and community-based electricity market designs. • Detailed review of user preferences applied to product differentiation mechanisms. • Detailed review of key performance indicators for consumer-centric markets. • Comparing the impact of user preferences in consumer-centric markets. • Assessment of consumer-centric markets through key performance indicators. The power system is facing a transition from its traditional centralized model to a more decentralized one, through the emergence of proactive consumers on the network, known as prosumers. This paradigm shift favors the emergence of new electricity market designs. Peer-to-Peer (P2P) based structures have been gaining prominence worldwide. In the P2P market, the prosumer assumes a more active role in the system, being able to directly trade its energy without the need for intermediaries. This paper contributes with a comprehensive overview of consumer-centric electricity markets, providing background on different aspects of P2P sharing, in particular the inclusion of peer preferences in the electricity trading process through product differentiation. A performance assessment of the different modeled preferences was carried out using key performance indicators (KPIs). Different user preferences under the product differentiation mechanism were simulated. The results demonstrate that consumer-centric markets increase the penetration of renewable energy sources into the network and tend to affect loads flexibility according to the renewable generation. [ABSTRACT FROM AUTHOR]

The problem of Database Query has always been considered from the user’s side. That is, the databases are always treated merely as the object of search, rather than being a subject or willing participants of an information exchange. This paradigm works when all participating databases belong to a single authority (such as a company) under which their participation is definitive and their contents completely open for the querying. Traditional single databases, federated databases, and even the new XML-based Internet databases subscribe to this user-oriented paradigm. However, emerging information enterprises are increasingly collaborative in nature, since they tend to involve, on a real-time and on-demand basis, a large number of databases belonging to many different organizations whose participation is conditional and case-by-case; e.g., drilling through supply chains. These collaborative queries deserve a new paradigm that equally account for the provider side. Research has shown that market-style self-allocation of users to providers is a promising approach to support such a paradigm. However, previous results of artificial markets are insufficient for global database query. Therefore, we develop an artificial market model to provide a Two-Stage Collaboration solution, where the first stage establishes optimal participation of databases for a search task, and the second executes the task in a traditional database query manner. The proposed model employs a new agent-based, peer-to-peer publish and subscribe approach to self-allocating database resources in an information enterprise. This approach promises to lead eventually to allocating other classes of information resources, as well. New results include (1) an agent model using a Metadatabase and an Agent-Base to create and manage large number of custom agents, (2) a peer-to-peer negotiation method, and (3) an open common schema design. The paper also provides an implementation scheme for developing the artificial market. Laboratory tests show that such a mechanism is feasible for large scale matching and negotiation as required by the first stage. The second stage employs mainly previous results established in the field. [ABSTRACT FROM AUTHOR]

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. • A network-aware heating market mechanism is proposed and analyzed. • We add explicit loss terms in peer-to-peer trades to reveal the origin of heat loss. • We propose an individual and a socialized method for loss cost allocation. • The market mechanism is compared to our benchmark in an illustrative case study. • Our market effectively promotes local consumption and thereby loss reduction. [ABSTRACT FROM AUTHOR]

• The peer-to-peer market with network constraints and user preferences was proposed. • We developed a distributed procedure and demonstrated the applicability of algorithm. • Algorithm avoids intermediate power flow calculations intrinsic to correction methods. With an increase of distributed generation growing attention is paid to the possibilities of its utilization in the network. The peer-to-peer market represents one of the possible ways to address this question. Largely driven by distributed ledger technologies, the peer-to-peer market architectures ignored network constraints for a long time, paying more attention to the organization of the financial transactions. In this paper we propose a peer-to-peer market design, incorporating network constraints, user preferences, and trade-independent network fees. In this way, we ensure a meeting of three requirements critical to the practical implementation of the peer-to-peer market as secure operation, consumer-centric nature of the market, and the provision of benefits for the grid. We develop a distributed procedure and demonstrate the applicability of the proposed algorithm using the IEEE 39-bus power system, and compare it with the correction-based algorithm. [ABSTRACT FROM AUTHOR]