Transactive Energy Research Articles

Benchmarking of Heuristic Algorithms for Energy Router-Based Packetized Energy Management in Smart Homes

This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.

Implementation design of energy trading monitoring application for blockchain technology-based wheeling cases

One obstacle to the energy industry’s tendency toward adopting renewable energy is the requirement for a monitoring system for energy transactions based on microgrids in the wheeling scheme (shared use of utility networks). The quantity of transaction expenses for each operational generator is not monitored in any case. In this project, a mobile phone application is developed and maintained to track the total amount of fees paid and received by all wheeling parties and the amount of electricity produced by the microgrid. In the wheeling case system research, the number of transaction costs, such as network rental fees, loss costs, and profit margins, must be pretty calculated for all wheeling participants. The approach created in this study uses a blockchain system to execute transactions, and transactions can only take place if the wheeling actor and the generator have an existing contract. The application of energy trading is the main contribution of this research. The created application may track energy transfers and track how many fees each wheeling actor is required to receive or pay. Using a security system to monitor wheeling transactions will make energy trades transparent.

Green electricity trading driven low-carbon sharing for interconnected microgrids

Green electricity becomes an inevitable trend to reduce carbon emissions. However, green certificate issuance based on power generation amounts restricts microgrids to participate in regional green electricity trading. In this context, a green electricity trading-driven low-carbon sharing approach for interconnected microgrids (IMG) is proposed. First, a new green electricity peer-to-peer (P2P) trading mechanism for regional IMG is proposed, where the energy sharing profile of each microgrid (MG) can be flexibly managed based on the interest. And the Nash equilibrium theory based on a non-cooperative game is applied to determine the trading capacity. Second, we define the green electricity value generated by P2P trading of renewable energy among IMG systems, where the green electricity value includes the value of traded green electricity, reduced carbon emission cost, and renewable energy subsidies. Especially, environmentally friendly factors are proposed to allocate the green electricity value between the buyer and seller, which can guide MGs to improve their energy composition and reduce carbon emissions. Third, a decentralized energy management approach is introduced to coordinate energy transactions among IMG systems based on the alternating direction method of multipliers (ADMM). The simulation analysis shows that the green electricity trading mechanism can reduce energy costs, carbon emissions and promote the consumption of green electricity.

Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage

Distributed peer-to-peer (P2P) energy trading can promote the localized balancing of power supply and demand, improve grid utilization efficiency, and ensure fairness. Shared energy storage (SES) enables users to withdraw electrical energy from shared batteries. This paper proposes a P2P energy trading model combined with SES and studies a cooperative surplus distribution mechanism based on the asymmetric Nash bargaining (ANB) theory. First, a cooperative model is established for enabling cooperation among sellers and buyers in a P2P energy trading system, offering a cooperative surplus due to cooperation. Secondly, by using the Asymmetric Nash Bargaining theory, a profit distribution model for the participants is constructed to ensure that the cooperation surplus can be fairly distributed among buyers and sellers. Finally, case studies are presented to verify the effectiveness of the proposed model. The simulation results show that the proposed cooperative method can improve the benefits of each entity and the overall benefits of the cooperative alliance. The proposed profit distribution model can reflect the difference in the contributions of sellers and buyers in the P2P energy trading system and fairly distribute the cooperation surplus. Furthermore, SES can improve the overall economics of DGs and consumers.

Optimal scheduling strategy and benefit allocation of multiple virtual power plants based on general nash bargaining theory

The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-to-peer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP’s contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources.

Optimal planning of distribution network with transactive energy: Review and framework

AbstractWith the emergence of distributed energy resources (DER) and prosumers in the distribution networks, the traditional centralised operation structure has transformed into the decentralised operation and transaction structure, which is regarded as the transactive energy market. Furthermore, the planning problem in the distribution network is deeply affected by the transactive energy market. First, the literature and studies on distribution network planning with DERs are reviewed. Moreover, the multi‐stakeholder operation and planning framework under the transactive energy market are analysed. The transactive energy market provides a peer‐to‐peer (P2P) transaction for third‐party prosumers. Several techniques for multi‐stakeholders planning and P2P transaction are introduced. Finally, a tri‐layer distribution network expansion planning (DNEP) framework integrated with virtual P2P transactions is proposed and analysed to accommodate uncertainties and postpone investment. The key technology and difficulties are also discussed.

Energy Storage Charging Pile Management Based on Internet of Things Technology for Electric Vehicles

The traditional charging pile management system usually only focuses on the basic charging function, which has problems such as single system function, poor user experience, and inconvenient management. In this paper, the battery energy storage technology is applied to the traditional EV (electric vehicle) charging piles to build a new EV charging pile with integrated charging, discharging, and storage; Multisim software is used to build an EV charging model in order to simulate the charge control guidance module. On this basis, combined with the research of new technologies such as the Internet of Things, cloud computing, embedded systems, mobile Internet, and big data, new design and construction methods of the energy storage charging pile management system for EV are explored. Moreover, K-Means clustering analysis method is used to analyze the charging habit. The functions such as energy storage, user management, equipment management, transaction management, and big data analysis can be implemented in this system. The simulation results of this paper show that: (1) Enough output power can be provided to meet the design and use requirements of the energy-storage charging pile; (2) the control guidance circuit can meet the requirements of the charging pile; (3) during the switching process of charging pile connection state, the voltage state changes smoothly. It can provide a new method and technical path for the design of electric vehicle charging pile management system, which can effectively reduce the system’s operation and maintenance costs and provide more friendly and convenient charging services.

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.

A Mycorrhizal Model for Transactive Solar Energy Markets with Battery Storage

Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators—increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets.

Mental accounting theory‐based bounded‐rationality trading strategy of prosumers in transactive energy market

AbstractIn the transactive energy market (TEM), prosumers are assumed to be rational. However, prosumers’ trading behaviours are affected by many factors (e.g. social relations, personal preferences, and habits). Their trading demands vary in benefits, comfort levels, and environmental consciousness, which show bounded rationality. Accordingly, this paper introduces the mental accounting theory to develop a trading decision‐making method by characterizing prosumers’ actual trading behaviours. It quantified prosumers’ motivations and psychological changes by the risk attitude and subjective value models in the process of multi‐account management. The updating rules of trading decisions and multi‐round bidding market mechanism are also designed to improve the social welfare and market efficiency. Case studies demonstrate that the proposed method can make prosumers trade in TEM under their actual intentions and bounded‐rationality. And the market mechanism can help to increase social welfare and market efficiency.

Feasibility analysis of solar PV system in presence of EV charging with transactive energy management for a community-based residential system

In this world, with enhancing environmental issues of greenhouses and fossil fuels-based systems in societies and assumptions of sustainable solutions includes Renewable Energy Technologies (RET) and Electric Vehicles (EV) are increased in the residential and commercial buildings. Moreover, Renewable Energy Resources (RES) have an exceptional milestone towards the world to supply energy from the energy market to the consumers without any scarcity and the EV charging facilities among the residential building is the most eminent research domain in the developing countries. This paper investigates the feasibility of solar energy for around 320 buildings include residential load with EV charging is considered for the selected location Vistara Residential Enclave at Madurai. Additionally, the optimal solution is performed using HOMER software for the proposed solar Photo Voltaic (PV), Battery Energy Storage System (BESS), grid extension, V2H process for increase the efficient energy management. Instead of, three scenarios are considered for analyse the technical, economic and environmental basis. Moreover, in the first one, off-grid/battery support system for only residential load using solar energy. Then, the second scenario is evaluated with on-grid/battery backup system by solar PV for household loads only. Finally, in the third scenario, on-grid/battery system for residential load as well as EV charging with considered V2H performance. Meanwhile, the multiple houses are confined to a residential site in the consideration of overall load system as well as divided into five zones. Moreover, the following these three scenarios cost and electrical parameters are predicted as with and without Transactive Energy Management System (TEMS) to overcome the peak load challenges. Therefore, the consequences of this study proposed that 36.90 % of Net Present Cost (NPC) is maximized and 51.87 % reduced Operating and Maintenance cost (O&M) between presence and absence of TEMS shows that overall system load with TEMS towards the localized grid is the best effective reliable system in an effective manner than five divided zones without TEMS.

An energy management system model with power quality constraints for unbalanced multi-microgrids interacting in a local energy market

As multi-microgrids become readily available, some limited models have been proposed that study operational and power quality constraints with local energy markets independently. This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interactions in a Local Energy Market (LEM) for unbalanced microgrids (MGs). The LEM consists of a pre-dispatch step and an energy transactions step (ETS). The ETS combines the MGs’ objectives while considering two strategies: minimize the cost of buyers or maximize the revenue of sellers. Our proposed model considers harmonic distortion and voltage limit power quality constraints in both steps. Moreover, we model operational constraints such as power flow, power balance, and distributed energy resources behaviors and capacities. We numerically evaluate the proposed model using three unbalanced MGs with residential, industrial, and commercial load profiles, where each microgrid manages its resources locally. Furthermore, we create two groups of cases to analyze the interactions in the local energy market. In the first group, the price of the DSO energy and the surplus from MGs to DSO are the same. The numerical results show that using the increasing revenue strategy promotes MGs to interact more while encouraging them to have high energy prices. When the reducing cost strategy is used, fewer energy interactions occur, and the price of MGs energy is encouraged to be lower.

Energy management of green charging station integrated with photovoltaics and energy storage system based on electric vehicles classification

As the number of electric vehicles (EVs) increases, EV charging demand is also growing rapidly. In the smart grid environment, there is an urgent need for green charging stations (GCS) to effectively manage the internal photovoltaic (PV), energy storage system (ESS), charging behaviors of EVs and energy transactions with entities. In this paper, a novel EV classification approach was proposed for GCS, of which the objective is to minimize the total cost of energy trading between charging station and entities. Based on EV charging requirements and risk preference for participating in demand response (DR), EVs are classified into three categories: (1) regular; (2) conservative; and (3) vehicle to grid(V2G). For different types of EVs, charging price schemes are proposed for GCS to stimulate more EVs to interact with the grid and duly dis-charging their power in the capacity of V2G runners. In dealing with the uncertainty issues caused by photovoltaic generation, chance-constrained and probabilistic sequence methods are employed to solve the proposed framework with satisfactory computational efficiency. Case studies based on genuine PV generation, load and pricing data are carried out to verify the proposed framework. It is demonstrated V2G EVs can dramatically reduce the total cost to the GCS. Besides, V2G EVs also can greatly reduce the charging cost compared to regular and conservative EVs.

Three-stage coordinated operation of steel plant-based multi-energy microgrids considering carbon reduction

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods.

Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers’ Loss of Life

This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers’ loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.

A Fair Risk-Averse Stochastic Transactive Energy Model for 100% Renewable Multi-Microgrids in the Modern Power and Gas Incorporated Network

The ever-growing multi-vector systems along with the penetration of renewable energy sources (RESs) entail significant changes for shifting from centralized, isolated, and passive structures to potentially distributed, hybrid, and autonomous modern multi-vector energy grids (MVEGs). This paper proposes a transactive energy (TE) solution for the techno-environmentaleconomic operation of multi-carrier multi-microgrids (MCMs) with 100 TE is advanced for making a fair economic model by developing the free multi-energy sharing area (MESA) for MCMs to allow them to exchange energy with the aim of pursuing their technical, environmental, and economic goals. As 100 uncertainties in the energy production sector, appropriately modeling such stochastic variations is a necessary step for obtaining realistic results in exploring the overall system. Thereby, the stochastic conditional value at risk (CVaR) technique is developed to model the risk of MCMs presence in energy interactions, in which scenario generation and reduction are performed by applying the seasonal autoregressive integrated moving average and fast forward selection methods. The problem is cast into a tractable mixed-integer linear programming by properly linearizing AC power flow and nonlinear gas equations that allows the system to extract confident results. The coupled structure of the modified IEEE 33-bus and 14-node gas systems is used as the test system for verifying the effectiveness of the proposed model. The results show the applicability of the proposed model in reliably integrating 100% RESs as well as procuring a fair condition for MCMs in the hybrid structure of the energy grid.

Trading in local markets: A review of concepts and challenges

With the development of decentralized sources of electricity generation, different ways of organizing electricity exchanges at the local level have been developed. The literature has studied extensively over the past decade how local exchanges can take place. This has resulted in different concepts reflecting different perimeters of study. However, the perimeters of these different concepts are not always well defined in the literature, which can lead to some con- fusion about the organization of the local market under study. There is a lack of harmonization because different terms may be used for the same concept or the same term may be used for several concepts. This paper aims to propose a harmonization of the different concepts for the study of local markets including local energy markets, peer-to-peer trading, local flexibility markets, microgrids, energy communities and transactive energy. These concepts are com- pared by identifying the characteristics of each. For this purpose, a literature review was per- formed in order to understand the context in which these concepts emerged and to identify their specific characteristics. Moreover, this paper proposes to analyze the economic challenges of local exchanges by identifying the economic incentives and solutions developed to make business models viable.

A data-driven stochastic energy sharing optimization and implementation for community energy storage and PV prosumers

The development of energy sharing community is demanded for the on-site consumption of distributed generations and efficient utilization of renewable energy. This paper proposes a two-stage stochastic energy sharing model considering the photovoltaic (PV) power uncertainties to minimize the social cost of PV prosumers and community energy storage (CES) in community. The proposed optimization model derives the optimal schedule of community under the worst case in the first stage. The regulation model for PV prosumers is formulated in the second stage to address the PV uncertainties including three regulation methods: the demand response, the charging/discharging schedule, and the transactive energy schedule. Particularly, a modeling approach based on the data-driven uncertainty set is introduced to find the worst probability distribution. The formulated two-stage problem can be solved by the column-and-constraint generation algorithm, where the coupled model is decomposed into a main problem and a sub problem and solved by iteration. Numerical simulation results show the effectiveness of the proposed energy sharing optimization model to decrease the energy cost. Experimental results demonstrate the feasibility of multi-ports energy hub used in energy sharing community.

A Transdisciplinary Blueprint for Energy Markets Modelled after Mycorrhizal Networks

The resource distribution strategies of trees and plants in the forest are applied here as inspiration for the development of a blueprint for transactive, hybrid solar and storage microgrids. We used the Biomimicry Institute’s Biomimicry Spiral and their toolbox as a design methodology to inform the structural and functional characteristics of this peer-to-peer microgrid energy market and propose its utility in addressing some of the challenges associated with grid integration of distributed energy resources (DERs). We reviewed literature from the ecological domain on mycorrhizal networks and biological market theory to extract key insights into the possible structure and function of a transactive energy market modeled after the mutualism between trees and mycorrhizae. Our process revealed insights into how overlapping, virtual energy markets might grow, contract, adapt, and evolve through a dynamic network-based protocol to compete and survive in rapidly changing environments. We conclude with a discussion of the promise and limitations involved in translating the derived conceptual blueprints into a cyber-physical system and its potential for deployment in the real world as a novel energy market infrastructure.

APPLICATION OF BLOCKCHAIN IN ENERGY

Blockchain or distributed ledger technology has attracted great interest from companies in the energy industry. The combination of blockchain and energy is undoubtedly one of the most striking technological innovations in recent years. A typical application area of blockchain in the energy industry is trading and credit. Stakeholders use blockchain technology to promote distributed or wholesale energy transactions and create virtual grids. Consumers can trade between their own devices and resources, with their neighbors and with the grid. The whole process can be automated through smart contracts. In this article, we will discuss some use cases related to the application of blockchain in the energy field.