Transactive Energy Market Research Articles

Enhancing Trust in Transactive Energy with Individually Linkable Pseudonymous Trading Using Smart Contracts

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility.

A resilience‐oriented restoration framework for multi‐area active distribution network following a disaster

AbstractA disaster disrupts energy trading between the distribution network (DN) and autonomous areas in the multi‐area active distribution network (MA‐ADN). Since these areas are self‐interested agents, the energy transaction cost between them should be considered when determining the grid restoration scheme. In this regard, a new three‐level resilience‐oriented restoration (TLROR) framework is proposed to optimally schedule available tie‐lines, and distributed energy resources (DERs) in the MA‐ADN, considering the autonomy and privacy of their ownership. In the first level of the proposed TLROR, an electrical price vector (EPV) is created with the contribution of different areas. In the second level, each area should calculate the amount of imported active and reactive power from the DN through its tie branches and save it in the transactive power list (TPL). Finally, at the third level, the transactive energy market is cleared by the DN operator considering economic issues and operating limits. The proposed method provides energy transactions between independent agents to enhance MA‐ADN resiliency, with the least information sharing, and privacy preserving. The effectiveness of the proposed method is verified using the modified IEEE 33‐bus MA‐ADN and a part of the DN in the Pol‐e Dokhtar in the Lorestan province of Iran.

Community-based transactive energy market concept for 5th generation district heating and cooling through distributed optimization

Fifth-generation district heating and cooling (5GDHC) is an emerging concept that exploits heat sharing among prosumers through a low-temperature bidirectional loop. Coordinating the energy interactions in such a system is a complex problem, typically solved through centralized optimization. In addition to privacy issues for prosumers, centralized optimization can prove too hard to solve for large 5GDHC systems and cannot establish a profit distribution between prosumers. To overcome these drawbacks, this paper develops a community-based transactive energy market framework based on distributed optimization of 5GDHC. The optimization is based on the Jacobi-proximal alternating direction method of multipliers. The approach relies on iterative interactions between the network coordinator and the prosumers, the former adjusting the internal price of thermal energy and the latter adapting their heating and cooling demand. A series of test-case 5GDHC networks involving houses, commercial buildings, data centers and central boilers and chillers were numerically simulated to demonstrate how the proposed framework performs, under three electricity tariff structures (constant, time-of-use or pay for peak). Results show that, for the cases tested, the gap between the proposed approach and global optimization is below 5% and that electricity savings compared to a situation without 5GDHC could reach 40%. The proposed method allowed to dynamically adjust the internal price of thermal energy and the prosumers' demand while considering their thermal dissatisfaction. By facilitating 5GDHC operation, this work could help their practical implementation in the future.

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Incentivising peers in local transactive energy markets: A case study for consumers, prosumers and prosumagers

AbstractA decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid‐edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer‐to‐peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1‐year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations.

Generalized Nash Equilibrium Analysis for Peer-to-Peer Transactive Energy Market Considering Coupling Distribution Network Constraints

Generalized Nash Equilibrium Analysis for Peer-to-Peer Transactive Energy Market Considering Coupling Distribution Network Constraints

A Decentralised Transactive Energy Market Considering Physical System Constraints

A Decentralised Transactive Energy Market Considering Physical System Constraints

Toward a Blockchain-Based, Reputation-Aware Secure Transactive Energy Market

The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market.

DSO Market for Transactive Scheduling of Electric Vehicle Charging Stations in Constrained Hierarchical Power Distribution and Urban Transportation Networks

The trend toward the deployment of electric vehicle charging stations (EVCSs) equipped with on-site distributed energy resources indicates potential transactive energy market (TEM) operations among EVCSs. However, unlike regular prosumers, EVCS operations are influenced by both the power distribution network (PDN) and the urban transportation network. In this paper, we propose a three-stage approach for solving the hierarchical energy scheduling and trading problem of EVCSs. At the first stage, the day-ahead traffic assignment problem of the transportation system is solved to determine individual EVCSs’ charging load. At the second stage, EVCSs solve their profit maximization problem to determine their energy scheduling and market participation decisions. Lastly, at the third stage, TEM among EVCSs is cleared by the distribution system operator (DSO). With the DSO shouldering both grid operator and market operator functions, the DSO problem is formulated as an unbalanced three-phase optimal power flow (OPF) model integrating a double-sided auction mechanism. To further improve the PDN operation, grid features such as network reconfiguration and tap changers are introduced into the OPF model. The effectiveness of the proposed framework is proved through case studies.

Transactive energy trading in distribution systems via privacy-preserving distributed coordination

This paper presents an approach to the transactive energy (TE) market that aims to improve the network efficiency and fairness of electricity market in distribution systems. The TE market is designed to allow subscribers to trade electricity in real-time based-on their energy consumption and generation forecast. The proposed framework utilizes distribution locational marginal pricing (DLMP), which accurately reflects the costs of electricity transmission and distribution, to fairly allocate these costs among subscribers. The Small Neighborhood Search Optimal Points (SNSOP) algorithm is then introduced to solve the challenge of finding optimal points for all participants in the TE market, as the maximum financial gain for one participant comes at the maximum financial loss of another participant. The results of the case study show that the proposed framework leads to a more efficient and reliable electricity network, by enabling decentralized control and reducing losses through optimal trades in the TE market.

Two-Tier Peer-to-Peer Transactive Energy Market

Two-Tier Peer-to-Peer Transactive Energy Market

Design and Implementation of a Blockchain-enabled Transactive Energy Market

Design and Implementation of a Blockchain-enabled Transactive Energy Market

Research on the Attack Strategy of Multifunctional Market Trading Oriented to Price

In the context of energy transformation and power market system reform, it is crucial to address the network risks associated with enhancing the integration of the “Energy–Information–Market” paradigm. This necessitates research on multi-energy market trading modes and the corresponding offensive and defensive technologies. This paper proposes a novel approach centered around a node-local Energy Hub (EH) that represents large industrial users with diverse energy demands. To facilitate multi-energy two-way trading, a price-oriented Transactive Energy (TE) market clearing strategy is developed. Building upon this transaction network framework, a data-driven attack strategy targeting the state estimator of the Transmission System Operator (TSO) is introduced and implemented in two stages, encompassing real-time topology estimation and False Data Injection attacks. By leveraging Matrix Transfer Entropy (MTE), the optimal attack target is identified to disrupt the economic stability of the system and the profit of the attacker increases significantly. The proposed attack strategy is validated through simulations conducted on a 30-node system, yielding conclusive evidence of its effectiveness while offering vital insights for system defense.

Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

AbstractResidential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers’ costs in the proposed model.

Grid-friendly peer-to-peer transactive energy market for inhibiting players’ strategic behavior

With the development of distributed renewable energy resources (RERs) in smart cities, the peer-to-peer (P2P) transactive energy market has become an effective way for RER owners to consume the surplus produced power. Nevertheless, the influence of grid usage costs in P2P energy trading has not been considered in prosumers’ strategic behaviors. To solve this issue, we analyze the network usage of P2P energy transactions and model a Nash–Stackelberg game to simulate the trading process of market participants. Firstly, we assume the prosumers can join the P2P energy market and determine their roles freely. The market manager adjusts the grid usage prices to influence the prosumers’ exchanged energy amounts. We then establish a monolithic VCG mechanism to inhibit the prosumers’ strategic behaviors while reducing the computational difficulty of the VCG mechanism. Finally, we develop a best-response (BR)-based algorithm to solve one energy prosumer’s cost minimization subproblem at a time to seek market equilibrium. Since the energy prosumers and market manager interact on the trading amount, the prosumers’ responses are modeled as bi-level mixed-integer nonlinear programming (BLMINLP). We employ the generalized Benders decomposition method to reduce the solution difficulty. Case studies verify the theoretical properties and show the practicability of the proposed algorithms.

Simultaneous AC/DC distribution for the sustainable operation of smart building clusters in hybrid urban microgrids

Hybrid AC/DC urban microgrids (HUMG) have emerged as a candidate solution to reliably, efficiently, and economically meet the increasing consumer electric demand and to ensure the quality of delivered power. Smart buildings (SB) are prevalent prosumers in HUMGs. To effectively utilise renewable resources in SBs, it is imperative to have assistance from battery energy storage systems (BESSs). Also, uncertainty in generation and demand necessitates real-time ancillary power support for SBs. Smart building interconnection forming SB clusters is an emerging solution for providing ancillary power support. Existing SB interconnection topologies require multiple power conversion stages, additional infrastructure, and underutilise the existing AC grid infrastructure. Most of these SB interconnections are not sustainable due to higher losses from more power conversion stages, increased cost, and excessive dependence on BESSs. A composite transmission system-based ancillary power dispatching architecture (CTS-APDA) for the sustainable operation of SB clusters is proposed in this paper. A multi-agent system based hierarchical control and decision logic in the CTS-APDA ensures the optimal ustilisation of battery resources. The operation of the proposed CTS-APDA and its efficacy as a fast-acting ancillary support is validated through extensive case studies. The proposed CTS-APDA will foster participation of electric vehicles in the transactive energy market of emerging SBs.

Network-Constrained Transactive Control for Multi-Microgrids-Based Distribution Networks With Soft Open Points

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for each individual microgrid and meanwhile ensure the voltage security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time corrective scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into a single-level mixed-integer second-order cone programming problem through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve the solving efficiency. Finally, numerical simulations on modified 33-bus and 123-bus test systems with multi-microgrids verify the effectiveness of the proposed framework.

A Review of Cybersecurity Concerns for Transactive Energy Markets

Advances in energy generation and distribution technology have created the need for new power management paradigms. Transactive energy markets are integrated software and hardware systems that enable optimized energy management and direct trading between prosumers. This literature review covers unresolved security and privacy vulnerabilities in the proposed implementations of such markets. We first performed a coarse search for such implementations. We then combed the resulting literature for references to privacy concerns, security vulnerabilities, and attacks that their system was either vulnerable to or sought to address. We did so with a particular focus on threats that were not mitigated by the use of blockchain technology, a commonly employed solution. Based on evidence from 28 peer-reviewed papers, we synthesized 14 categories of concerns and their proposed solutions. We found that there are some concerns that have been widely addressed, such as protecting trading history when using a public blockchain. Conversely, there were serious threats that are not sufficiently being considered. While a lack of real-world deployment has limited information about which attacks are most likely or feasible, there are clear areas of priority that we recommend to address going forward, including market attacks, false data injection attacks, single points of failure, energy usage data leakage, and privacy.

Decentralized transactive energy market framework under network constraints to deal with technical issues in a radial distribution network

A growing number of distributed energy resources (DERs) connected to the distribution network presents operational challenges. Therefore, it is crucial to develop an effective framework to address these challenges. In this paper, a novel transactive energy market (TEM) framework is proposed in two levels based on the concept of a network usage fee (NUF) to address technical issues. We perform the peer matching process using a primal-dual algorithm at a lower level. We consider distribution system operation (DSO) operations (e.g., Volt/VAR control and network reconfiguration) and NUF to maintain network constraints and to provide an optimal network topology to facilitate energy trading. The proposed framework has been tested on an IEEE 33-bus distribution network. A key issue relating to the coordination of DERs in distribution networks is addressed in the results. In the proposed NUF, the sensitivity analysis is used to fully diagnose the portion of each TE agent in power loss, congestion, and voltage violations. A fair penalty or incentive signal can be generated to ensure that the network operates within the limits. Furthermore, the proposed framework ensures that the privacy of agents is protected. DSO operation facilitates the trading of energy and minimizes interference with their schedules.

An optimal transport theory based approach for efficient dispatch of transactions in energy markets

AbstractNowadays, transactive energy markets (TEMs) are emerging as interesting frameworks in deregulated power markets to control the balance of supply and demand in the entire electrical network. Due to wide deployment of renewable energy resources, grid connected micro‐grids, and open access transmission and distribution networks, the planning and operation of TEMs become complex. So, an efficient optimal dispatch model for TEMs should be developed to achieve the objectives of TEMs, such as feasible sizes of transactions and optimal dispatch of these transactions with minimal operating costs. The transactive dispatch problem is similar to the resource allocation/matching problem. Recently, optimal transport (OT) has received significant attention in various fields including optimization theory and resource matching problems due to its potency and relevance in modeling and optimization. An OT‐based approach is proposed here for optimal dispatch of transactions in energy markets while minimizing the cost of transactions considering the operating constraints of the system. The proposed approach can efficiently determine the feasible sizes of transactions without any security issues. The optimal solutions of the OT‐based approach are obtained using a Sinkhorn iterative technique. Also, the load uncertainties are considered in this work to analyse the impacts of load uncertainties on the optimal dispatch of transactions. The numerical simulation results on the modified IEEE 9‐bus system, modified IEEE 57‐bus system, modified IEEE 118‐bus system, and Indian Northern Regional Power Grid (NRPG) system illustrate the efficacy of the proposed OT‐based framework.