Transactive Energy Systems Research Articles

Transactive Energy System Concept in Power Distribution System- A Review

Transactive Energy System Concept in Power Distribution System- A Review

BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading

Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market.

Resilience-Driven Scheme in Multiple Microgrids With Secure Transactive Energy System Framework

Resilience-Driven Scheme in Multiple Microgrids With Secure Transactive Energy System Framework

Smart transactive energy based approach for planning and scheduling in multi-looped microgrid distribution network across planning horizon

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Transactive Energy System Deployment Over Insecure Communication Links

In this paper, the privacy and security issues associated with the transactive energy system (TES) deployment over insecure communication links are addressed. In particular, it is ensured that 1) individual agents’ bidding information is kept private throughout hierarchical market-based interactions; and 2) any extraneous data injection attack can be quickly and easily detected. An implementation framework is proposed to enable the cryptography-based enhancement of privacy and security for the deployment of any general hierarchical systems including TESs. Under the proposed framework, a unified cryptography-based approach is developed to achieve both privacy and security simultaneously. Specifically, privacy preservation is realized by an enhanced Paillier encryption scheme, where a block design is proposed to significantly improve computational efficiency. Attack detection is further achieved by an enhanced Paillier digital signature scheme, where a stamp-concatenation mechanism is proposed to enable detection of data replace and reorder attacks. Simulation results verify the effectiveness of the proposed cyber-resilient design for transactive energy systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by addressing the issues of cyber resiliency for practically deploying transactive energy system (TES) but it is also applicable to the problem of enhancing the privacy and security for any general hierarchical control systems. TES is an emerging control approach that engages energy suppliers and customers through market operations and uses the price to optimally allocate energy resources. While it has been shown to be promising for power system applications, the underlying market-based interactions raise significant concerns of privacy (data leakage) and security (data tampering). However, existing TES works only focus on the coordination mechanism instead of privacy and security issues. This paper proposes a new cryptography-based TES design for practical deployment. Specifically, to protect privacy, individual supply and demand amounts to be exchanged are all encrypted in a particular way such that the original amounts cannot be inferred from the encrypted amounts, while the desired computation for setting the market clearing price can be carried out over the encrypted amounts, thus generating an encrypted result which, when decrypted, matches that of the same computation over the original amounts. To achieve security, for each exchanged data, its sender generates a particular digital signature which is exchanged together with the data. This enables the receiver to automatically detect the integrity by checking whether a mathematical relationship holds for the pair of data and signature. In our future research, we will investigate more challenging scenarios where some suppliers and customers themselves could be corrupted and purposely submit distorted amounts.

Evaluation of Smart Contract Vulnerability Analysis Tools: A Domain-Specific Perspective

With the widespread adoption of blockchain platforms across various decentralized applications, the smart contract’s vulnerabilities are continuously growing and evolving. Consequently, a failure to optimize conventional vulnerability analysis methods results in unforeseen effects caused by overlooked classes of vulnerabilities. Current methods have difficulty dealing with multifaceted intrusions, which calls for more robust approaches. Therefore, overdependence on environment-defined parameters in the contract execution logic binds the contract to the manipulation of such parameters and is perceived as a security vulnerability. Several vulnerability analysis tools have been identified as insufficient to effectively identify certain types of vulnerability. In this paper, we perform a domain-specific evaluation of state-of-the-art vulnerability detection tools on smart contracts. A domain can be defined as a particular area of knowledge, expertise, or industry. We use a perspective specific to the area of energy contracts to draw logical and language-dependent features to advance the structural and procedural comprehension of these contracts. The goal is to reach a greater degree of abstraction and navigate the complexities of decentralized applications by determining their domains. In particular, we analyze code embedding of energy smart contracts and characterize their vulnerabilities in transactive energy systems. We conclude that energy contracts can be affected by a relatively large number of defects. It also appears that the detection accuracy of the tools varies depending on the domain. This suggests that security flaws may be domain-specific. As a result, in some domains, many vulnerabilities can be overlooked by existing analytical tools. Additionally, the overall impact of a specific vulnerability can differ significantly between domains, making its mitigation a priority subject to business logic. As a result, more effort should be directed towards the reliable and accurate detection of existing and new types of vulnerability from a domain-specific point of view.

Auction Theory and Device Bidding Functions for Transactive Energy Systems: A Review

Auction Theory and Device Bidding Functions for Transactive Energy Systems: A Review

Co-simulation platform for the assessment of transactive energy systems

The integration of distributed energy resources (DERs) into smart grids provides great flexibility in terms of ancillary services like the restoration of power supply, regulation of voltage levels, and reactive power support, among others. In this context, this work introduces a simulation model applied in the conception of a transactive energy system (TES) architecture. The proposed approach allows for the integration of distinct domains associated with smart grids considering the joint use of simulation tools that constitute a multidomain system, thus enabling the assessment of control actions, optimization algorithms, load flow, and communication infrastructure. The Simulation Model for Transactive Energy Systems (SIMTES) framework relies on a multiagent system (MAS) and consists of two main stages: operation scheduling and real-time operation. The obtained results demonstrate the applicability of SIMTES, showing that the use of DERs associated with the grid and prosumers clearly influences energy pricing in electricity markets.

Game-theoretic peer-to-peer solar energy trading on blockchain-based transaction infrastructure

The growing share of distributed, renewable resources in energy mix to combat climate change calls for a significant efficiency improvement through market-based solutions such as peer-to-peer (P2P) transactive energy systems. This paradigm shift requires an energy market that supports its functions through well-designed market rules and transaction infrastructure—the two ingredients that form a marketplace. Blockchain technology can facilitate the implementation of both market rules and transaction infrastructure for newly emerging P2P transactive energy markets. In this paper, we propose a model for the design of a transactive energy marketplace using blockchain as transaction infrastructure based on game-theoretic market rules that incentivize a more sustainable energy generation and consumption behavior while preserving the privacy of economic agents in the market. We propose a novel consensus mechanism named Proof-of-Reserve (PoR) to perform consumer-prosumer transactions and block mining activities on a distributed ledger. The model is numerically analyzed on a test system to illustrate its effectiveness. The results demonstrate the effectiveness of game-theoretic modeling in establishing market equilibrium and showcase the efficiency of blockchain technology in facilitating a functional, decentralized transaction settlement process.

Detection and Analysis of Ethereum Energy Smart Contracts

As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.

Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review

Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.

Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity.

Transactive energy system: Concept, configuration, and mechanism

Transactive energy systems (TESs) combine both economical and control mechanisms, and have become promising solutions to integrate distributed energy resources (DERs) in modern power systems. This article will introduce the basic concept of a TES, including its definition, process, time scale, and benefits. The configuration of the TES is then described in detail from the perspectives of the physical system, information system, transaction system, and regulatory system. The transaction mechanism allows participants (e.g., customer, generator, transmission operator, marketer, etc.) to conduct various transactions with any other party to the extent that regulatory policy permits. Transactive control is regarded as one of the most advanced approaches to realize the full response potential of flexible devices and respect the end user’s privacy, preference, and free will. Finally, some challenges to the development of TESs that arise from the limitations of current equipment level and methodological concepts will be discussed. In summary, TESs provide a more efficient, fair, and transparent environment for participants to promote DERs utilization, improve market efficiency, and increase economic benefits.

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile

Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept.

The Role of Transactive Energy in the Future Energy Industry: A Critical Review

Transactive energy is a highly effective technique for peers to exchange and trade energy resources. Several interconnected blocks, such as generation businesses, prosumers, the energy market, energy service providers, transmission and distribution networks, and so on, make up a transactive energy framework. By incorporating the prosumers concept and digitalization into energy systems at the transmission and distribution levels, transactive energy systems have the exciting potential to reduce transmission losses, lower electric infrastructure costs, increase reliability, increase local energy use, and lower customers’ electricity bills at the transmission and distribution levels. This article provides a state-of-the-art review of transactive energy concepts, primary drivers, architecture, the energy market, control and management, network management, new technologies, and the flexibility of the power system, which will help researchers comprehend the various concepts involved.

Multiagent Bayesian Deep Reinforcement Learning for Microgrid Energy Management Under Communication Failures

Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid. Although there have been many works on MG energy management, most studies assume a perfect communication environment, where communication failures are not considered. In this article, we consider the MG as a multiagent environment with IoT devices in which AI agents exchange information with their peers for collaboration. However, the collaboration information may be lost due to communication failures or packet loss. Such events may affect the operation of the whole MG. To this end, we propose a multiagent Bayesian deep reinforcement learning (BA-DRL) method for MG energy management under communication failures. We first define a multiagent partially observable Markov decision process (MA-POMDP) to describe agents under communication failures, in which each agent can update its beliefs on the actions of its peers. Then, we apply a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (DDQN) architecture for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value estimation in BA-DRL, and propose a belief-based correlated equilibrium for the joint-action selection of multiagent BA-DRL. Finally, the simulation results show that BA-DRL is robust to both power supply uncertainty and communication failure uncertainty. BA-DRL has 4.1% and 10.3% higher reward than Nash deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (Nash-DQN) and alternating direction method of multipliers (ADMM), respectively, under 1% communication failure probability.

Designing a two-stage transactive energy system for future distribution networks in the presence of prosumers’ P2P transactions

Electric distribution systems are becoming bidirectional and active due to rise of distributed energy resources, electric vehicles and prosumers. In this environment, a transactive energy system is required to not only coordinate the network with respect to the technical constraints, but also model P2P transactions between end-users, taking into account their data privacy and scalability issues. Therefore, a new two-stage transactive energy system is developed to balance the distribution network considering the behavior of different participants. At stage one, the role of orchestrator is modeled to manage the distribution network and its constraints by purchasing energy from up-stream grid, defining dynamic distribution locational marginal prices (DLMPs) or tapping on its storages. At stage two, prosumers make distributed decisions to use grid-sourced/self-generated energy or participate in P2P transactions based on the received DLMPs from stage one and their preferences. This two stage mechanism is solved through an iterative process until stopping criteria are met. The results indicate reduction in total import cost and energy from grid, compared to when no P2P transaction is considered. It is also shown that the proposed model converges to the optimal solution in proper time when the number of participants is increased.

Optimization of transactive energy systems with demand response: A cyber‐physical‐social system perspective

AbstractWith the increasing penetration of renewable energy, a new type of energy system, transactive energy systems (TES), has emerged. This study investigates the challenges of optimally operating a TES distribution system with demand response (DR) from the cyber‐physical‐social system (CPSS) perspective. A TES optimization framework that integrates artificial systems, computational experiments, and parallel energy theory for modelling DR, via parallel system theory, is introduced. A data‐driven artificial DR system is created and modelled using limited data. In the computational experiment, a complete information Stackelberg game model for the distribution network operator and the artificial DR system is built. This simulates the response relationship between the distribution network operator and the electricity consumer under different price conditions. In the parallel energy optimization model, a multi‐time scale energy optimization method which considers day‐ahead and intraday scenarios, the interaction between the actual TES and the artificial DR system is shown. Finally, empirical data from the Henan province in China is used as a case study to verify the effectiveness of the optimization method proposed in this study.

Planning, operation, and trading mechanisms of transactive energy systems in the context of carbon neutrality

Planning, operation, and trading mechanisms of transactive energy systems in the context of carbon neutrality