Transactive Energy Research Articles

Blockchain-Enabled Microgrids: Toward Peer-to-Peer Energy Trading and Flexible Demand Management

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management.

Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach

The uncertainties induced by electric vehicle (EV) demand and market operations pose huge challenges to the optimal bidding decision of the EV aggregator (EVA) in the day-ahead (DA) market. Note that a risk-neutral bidding solution with the expected cost minimization may make the EVA suffer a high financial loss in the market. As such, in this study, a risk-averse bidding strategy is developed to support the EVA to participate in the market via the conditional value-at-risk (CVaR) to handle market price volatility. Specifically, the strategy minimizes the CVaR metric over a collection of real-time (RT) clearing scenarios to reduce the energy transaction risk of the EVA in the market. Moreover, the model is reformulated as a linear programming (LP) problem that is mathematically tractable and can be efficiently solved. The proposed solution is extensively assessed through experiments based on the PJM market against the risk-neutral bidding strategy as a comparison benchmark. The numerical results reveal that the proposed risk-averse bidding strategy outperforms the risk-neutral one in terms of risk control, which enables the EVA to avoid suffering a huge financial loss incurred by RT clearing prices. In addition, the transformed LP model is superior to the original model in computational efficiency.

Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed.

A task matching model of photovoltaic storage system under the energy blockchain environment - based on GA-CLOUD-GS algorithm

Photovoltaic storage system (PVSS) has been spawned with the combined application of photovoltaic (PV), energy storage (ES) and energy blockchain (EB), which has also made important contributions to the energy structure adjustment, energy transaction security and ecological environment protection. The establishment of a reasonable task matching mechanism can further improve the operation efficiency and quality of the system under the EB environment, and this paper consequently constructs a task matching model of PVSS based on GA-CLOUD-GS algorithm. Firstly, the decision-making committee is set up and the index system is determined for the to-be-matched task. Secondly, the Genetic Algorithm (GA) comprehensive weighting method is introduced to integrate the subjective and objective weights to achieve the weight optimization. Thirdly, the cloud model is used to obtain the preference order and priority order of the to-be-matched subjects. Fourthly, the Gale-Shapley (GS) algorithm is used to obtain the task matching results. Finally, the operability, the stability and the effectiveness of the model are separately verified through numerical simulation, sensitivity analysis and comparative analysis. Corresponding suggestions for the task matching of PVSS in the EB environment are also put forward according to the research content.

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

The notion of prosumers, people who create and consume energy, has emerged in the energy sector as a result of the fast integration of renewable energy sources and advances in digital technology. Platforms for peer-to-peer (P2P) energy trading have come to light as viable ways to allow prosumers to conduct direct energy transactions within small groups. Existing P2P trading models, however, frequently lack an optimized approach to maximize the advantages for prosumers. In this article, we provide a novel six-segment strategy (SSS) that is intended to increase prosumers′ ability to maximize their benefits in P2P energy trading systems. Additionally, the SSS promotes prosumers′ active market engagement by supporting community-driven energy exchanges. Through extensive mathematical modeling, simulations, and case studies, we demonstrate the effectiveness of the six-segment strategy in enhancing the economic benefits of prosumers participating in P2P energy trading. The proposed optimization strategy holds the potential to revolutionize the energy landscape by promoting more sustainable and consumer-centric energy trading. The cost savings of 12.9% are distributed among prosumers by the SSS, which is an improvement compared with previously proposed strategies.

Comparison on Proof of Work Versus Proof of Stake and Analysis on Why Ethereum Converted to Proof of Stake

As approaching the era of Ethereum 2.0, the already unique and innovative cryptocurrency will witness a game-changing upgrade, and in this context, there is a lively discussion of the Proof of Work (PoW) and Proof of Stake (PoS) consensus mechanism. Moving from a PoW consensus mechanism to a PoS consensus mechanism is one of the most anticipated changes in this update. A close comparison of the two most popular consensus mechanisms, PoW and PoS, provides some strengths and drawbacks to these mechanisms. This comparison then becomes the building block to identifying the reasons behind Ethereum's upgrade to Proof of Stake. With the help of the comparison, this paper identifies a few drawbacks of the PoW consensus mechanism that Ethereum is currently facing. This paper finds that Ethereum's shift to PoS has indeed reduced the drawbacks of the Proof of Work consensus mechanism, mainly in terms of energy consumption, transaction cost, and confirmation speed.

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.

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Integrating flexibility and energy local markets with wholesale balancing responsibilities in the context of renewable energy communities

Prosumers can organize themselves in collective self-consumption (CSC) structures and renewable energy communities (RECs) to share energy they produce locally. In addition, through their contracted balancing responsible party (BRP), i.e., retailer and aggregator, they could become flexibility providers for system services to solve, for example, local grid constraints. Since CSC and REC structures are progressively being regulated in many countries, local energy markets (LEMs) and local flexibility markets (LFMs) to be developed with these structures should find the way to comply with existing CSC rules to settle energy transactions and flexibility activation, both, locally and with the wholesale markets (WSMs) settlement, and the existing barriers and regulatory improvements should be identified to allow future implementations. Indeed, the integration of local and WSMs is still a matter of development, demanding innovative solutions, one of the main issues being, for example, the impact of the flexibility activation by one BRP into another BRP’s expected delivery commitment in the WSM. This work proposes innovative designs for LEM and LFM based on common CSC rules of existing regulations, and a conceptual approach to integrate them together and with the WSM balancing responsibilities of the BRPs involved, identifying existing regulatory barriers. While many LEMs in the literature operate as WSMs, with future markets and delivery commitments for prosumers, we propose the use of a post-delivery LEM that can be cleared even after the delivery of energy, which strongly simplifies prosumers participation avoiding the need of these a priori unrealistic commitments. The business model, the main roles involved, and the contractual framework to connect the BRPs while allowing prosumers to freely contract the BRP of their choice for both energy supply and flexibility provision are described and can serve as a guide for future regulatory improvement of the common regulatory frameworks.

Safe resource management of non-cooperative microgrids based on deep reinforcement learning

This paper solves a bi-level optimization problem in which at the first level, the distribution system operator (DSO) as a retailer tends to extract selling energy price signals so that increases its profit and reduces the peak-to-average ratio (PAR) of the MMG transactive energy; and at the second level energy management problems of non-cooperative networked microgrids (MGs) are solved individually to minimize their cost. This game theoretically problem is known as the Stackelberg game and has been solved with classical optimization methods like Karush–Kuhn–Tucker (KKT) method previously. To solve this problem with the classical KKT method, the MGs’ information should be provided for the DSO as a game starter, which is not the MGs’ desire for their privacy concerns. With the aim of preserving the privacy of MGs, this paper has developed a decision-making mechanism based on reinforcement learning (RL) to help the DSO extract energy prices individually for each MG. Also, the deep neural network (DNN) is used as a practical tool to predict MGs’ behavior in response to signal price. Furthermore, in the model used in this study, energy storage systems (ESSs) are dedicated to MGs which makes the model non-linear, thus the proposed method unlike the classic method is able to solve such a problem. The results obtained from the comparison between the classic and proposed method show that the implementation of this machine learning-based method is not only accurate enough but also is faster than the classical method, in addition to its privilege of privacy-preserving. Finally, a sensitivity analysis has been conducted to evaluate the DSO profit and PAR under different weighting factors of the objective function to obtain an appropriate performance.

Cybersecurity in Smart Grids: Protecting Critical Infrastructure from Cyber Attacks

The increasing digitalization of smart grids has brought numerous benefits in terms of efficiency, sustainability, and reliability, but it has also exposed these critical infrastructures to significant cybersecurity risks. This study investigates the cybersecurity challenges faced by smart grids, focusing on the vulnerabilities present in communication systems, control networks, and endpoint devices. The problem statement revolves around the growing complexity of smart grid infrastructures, which have become prime targets for cyberattacks such as denial-of-service (DoS), malware, and data breaches, threatening grid stability, data integrity, and national security. The primary objective of this study is to evaluate the current cybersecurity methods used in smart grids, such as encryption, firewalls, intrusion detection systems (IDS), and emerging technologies like artificial intelligence (AI) and blockchain. The study also aims to assess regulatory frameworks, such as the National Institute of Standards and Technology (NIST) and North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) standards and identify gaps in policy that need addressing. The results of the study demonstrate that AI-based anomaly detection systems significantly reduce attack detection times, averaging 2.5 seconds, with a low false positive rate of 7%. Blockchain technology, while increasing energy overhead by 15%, provides enhanced security for decentralized energy transactions. Multi-factor authentication (MFA) proved effective, blocking 98% of unauthorized access attempts. However, regulatory gaps were identified, especially in real-time monitoring and incident response. The study concludes that by adopting a layered security approach, integrating AI and blockchain technologies, and strengthening regulatory frameworks, smart grids can achieve a 75% improvement in cybersecurity efficacy. Continued efforts to address challenges such as legacy system integration, resource limitations, and evolving cyber threats are essential to securing the future of energy infrastructure.

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

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

A cooperative model of photovoltaic and electricity-to- hydrogen including green certificate trading under the conditional value at risk

Cooperation in energy systems is no longer limited to the distribution of electricity, and more attention is paid to the trading of green certificates (GCs). This paper proposed a cooperative method for photovoltaic (PV) and electric-to- hydrogen (EH) trading, including GC trading under risk management. First, a novel PV and EH model is established and the cooperation mechanism is analyzed. Meanwhile, PV and EH models were risk-controlled using the conditional value at risk to reduce the impact of the uncertainty of PV electricity and EH loads. Then, the PV-EH cooperative model was established based on cooperative game theory; this was then divided into two subproblems of “cooperative benefit maximization” and “transaction payment negotiation,” and the above two subproblems were solved distributively by alternating direction multiplier method (ADMM). Only energy transactions and price negotiations were conducted between the PV and EH, which can protect the privacy and confidentiality of each entity. Finally, the effectiveness of the cooperation model was verified using a practical engineering case. The simulation results show that the cooperation of the PV-EH can significantly improve the operational efficiency of each entity and the overall efficiency of the cooperation and realize the efficient redistribution of electricity and GC.

A novel loss allocation method applicable for any desired network topology

Due to the expansion of power networks in response to the increase in total load demand, the complexity regarding power systems transmission networks’ configuration has been increased. Similarly, with the advent of local energy markets and transactive energy concept, power distribution networks are undergoing a fundamental change. In this respect, numerous agents are transacting power within both distribution and transmission networks. Therefore, as more power is transacted among agents in different energy networks, power networks face higher power losses. Dealing with the financial outcomes of these losses, a general accurate yet fast method for loss allocation in power networks is of utmost importance. In response, this paper proposes a novel loss allocation framework based on the power tracking method that is applicable for any desired network topology within both transmission and distribution networks. In this regard, at first, a new method is proposed to obtain how the optimal scheduled generation of each production unit should be allocated to each specific load of the network. Then, it is obtained that how these transactions are flown in each line of the network. In the next step, the exact losses in each line resulted from distinct transactions between each pair of agents in the network are obtained. Finally, how each specific transaction contributes to the total losses of the network can be calculated. By implementing the proposed framework on a modified IEEE 14-bus test system and a 69-bus IEEE distribution network, the applicability and effectiveness of the proposed method is demonstrated by the numerical results. The results indicate that the proposed method can be implemented in any network and with any scale.

Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project.

Multi‐stage energy‐risk adjustments using practical Byzantine fault tolerance consensus for blockchain‐powered peer‐to‐peer transactive markets

AbstractThe energy risk associated with distributed energy resources is inevitable in peer‐to‐peer (P2P) transactive energy markets owing to mismatches between energy commitments and metered measurements. However, adjusting these possible mismatches by progressive revision of the energy commitments in the rolling time horizon mitigates the energy risk, and thereby mitigates the financial risk for prosumers. In this study, the conditional value at risk (CVaR) is used to estimate the risk value for each prosumer. The energy offers that are riskier than CVaR‐based threshold values are reduced in an ‘adjustment bid’. A new pricing mechanism for these adjustment bids is introduced, which varies with historical deviations of a prosumer from energy commitments. This market framework and pricing mechanism are simulated through a blockchain network hosted on a Python Django server using the practical Byzantine fault tolerance consensus algorithm to guarantee network immutability and data privacy. Efforts to mitigate such mismatches between ex ante and ex post energy values incentivise risk‐aware participation in P2P markets. In addition, the welfare of both prosumers and consumers improves with their participation in the proposed market framework. Furthermore, implementing a network using blockchain technology guarantees the privacy of bidding data and provides a secure transaction platform.

A Stackelberg game-based approach to transaction optimization for distributed integrated energy system

Distributed integrated energy system (DIES) will become the main energy supply method for end-users, however, the volatility of renewable energy makes energy consumption more difficult. To address the problem, this paper proposed an optimization method of DIES energy transaction based on Stackelberg game. Firstly, the typical structure of DIES is studied and a energy transaction framework of electric-heat energy is proposed by introducing integrated energy operator(IEO); secondly, with IEO as the leader and DIES as the follower, considering the highest profit of IEO and the lowest cost and carbon emission of DIES as the objectives, and combining the operational constraints of different subjects, a one-master-multiple-slave Stackelberg game model is established; thirdly, the existence of Nash equilibrium of this game model is demonstrated, and a solution algorithm based on Particle swarm optimization nested Non-dominated Sorting Genetic Algorithm-II is proposed; Finally, to verify the validity of this model, two modes are designed and simulated, and the results show that the proposed method can improve the renewable energy consumption by 2.92%, reduce the DIES energy cost by 3.94%, and reduce the interactive power with the grid by 30.91%, while the sensitivity analysis shows that the method can better cope with energy price changes.

Rethinking green investment and corporate sustainability: the south European countries experiences during the COVID-19 crisis

PurposeThe COVID-19 pandemic has generated a crisis that has hit the European economy and the currently existing systems. To cope with the crisis, Europe has started an investment aiming the energy transition and crisis. Portugal, Spain and Greece have received the approval of their National Recovery Plans from the European Commission, with a definition of spending up to 2026 through the European Union (EU) Next Generation Found. The study investigates whether the Green Deal policies are relaunched by the plans financed and whether the pandemic has changed and conditioned the priorities of the energy transaction. The study uses the lens of corporate social responsability (CSR) and relapse measurable across the Sustainable Development Goals (SDGs).Design/methodology/approachThe Green Deal policies supported by new European investments in the three countries were analyzed through a content analysis (CA) technique to investigate the associated practical and theoretical elements.FindingsThe energy theme has a relevance compared to other issues in the investment plans envisaged in Greece, Portugal and Spain. The analysis highlights energy efficiency, sustainable energy and reduction of consumption among the main themes. Energetically, sustainable building plays a central role. The study highlights the relationship between Green Deal policies, CSR, SDGs and management tools adopted.Originality/valueThe study strengthens the relationship between the Green Deal, CSR and SDGs by identifying policies that have already been implemented and theoretical and practical gaps on which politicians and scholars will have to investigate and support in the process of development and continuity of the identified pillars.

Blockchain Technology in Various Fields: Applications, Challenges, And Future

Blockchain technology has revolutionized many industries and has great potential outside of finance. This paper explores blockchain's applications and challenges in agriculture, electricity, transportation, healthcare, and finance. Blockchain technology can track agricultural product origin, quality, and safety to improve supply chain transparency in agriculture. Blockchain can also reduce intermediaries, improve payment systems, and expand financing. Blockchain can enable decentralized electricity management, peer-to-peer energy transactions, and lower transaction costs. It could boost renewable energy integration, grid efficiency, and energy access for underserved communities. Blockchain technology can improve transportation supply chain visibility and reduce fraud by providing a shared, tamper-proof ledger to track goods and prevent unauthorized access. Blockchain can secure, interoperable, and improve patient privacy in healthcare. It could let patients share their health data with providers and researchers while protecting their privacy. Blockchain implementation is also tricky. Scalability, interoperability, compliance, and data privacy Blockchain solutions must handle large amounts of data, integrate with existing systems, comply with laws and regulations, and protect sensitive data. Further research and development are necessary to explore blockchain technology's possibilities in these fields fully.

Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture’s reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture.