Energy Trading Scheme Research Articles

Integration of hydrogen storage system and solar panels in smart buildings

In the residential area, the cost of the electricity is at the maximum level during the peak demand periods. Regarding this, the peer-to-peer (P2P) energy trading strategy is a hopeful and practical solution to deal with this problem. Also, in the P2P home energy management system, the buildings have become more flexible by sharing their powers to meet their electricity demands. In this work, we have considered three buildings, in which two of them are equipped with solar panels, and in the third one, the hydrogen source is established. In the proposed residential zone, the considered buildings are connected in order to perform P2P transactions to reduce the energy billing. The operator of the system is wholly investigated under uncertain conditions based on the downside risk constraint (DRC) method in both risk-averse and risk-neutral models. With the help of a P2P energy trading strategy as well as renewable energy resources, we can provide electricity to customers in remote areas where it is not possible to supply electricity or is not economically beneficial. Based on the obtained results, the function of the proposed system has manifested in an acceptable manner. For instance, in the solar panel function in the second building, the amount of generated solar power is 30 kW in the risk-averse model, whereas, in the risk-neutral model, it is 18 kW. On the other hand, the level of consumed electricity by the electrolyzer system is generally lower in the risk-averse model than risk-neutral model in the second season.

Pool trading model within a local energy community considering flexible loads, photovoltaic generation and energy storage systems

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.

Energy trading strategy for storage-based renewable power plants

Despite the continuous growth and the widespread support of renewable energy sources, solar and wind power plants pose new challenges for Transmission System Operators and Distribution System Operators. Their uncontrollability limits their applicability; therefore, to encourage their further growth, fundamental modifications are needed. The research presented in this paper focuses on the predictive control of storage-based renewable power plants, and suggests a new model for profit optimization. Profit optimization is based on electricity price prediction and effective trading strategies that match the projected electricity prices. For the electricity price prediction, a recurrent Long Short-Term Memory neural network was developed and fine-tuned. For the optimization of the electricity trading, two trading strategies, namely an adaptive gradient-descent method and a differential evolution method were developed. Both optimization techniques were tested on mathematical models of most commercially available hybrid inverter systems and one year of historical data of electricity prices. As a result, a novel model predictive control workflow and sizing guide is proposed, which may significantly increase the profit generated by the system.

Improving Physical Layer Security in IRS-Aided WPCN Multicast Systems via Stackelberg Game

This paper investigates an intelligent reflecting surface (IRS) aided secure wireless powered communication network (WPCN), where a transmitter first harvests energy from a power station (PS), and then uses the collected energy to transmit information to multiple internet of things (IoT) devices in the form of multicast in the presence of multiple eavesdroppers. An IRS is deployed to enhance the efficiency of wireless energy transfer (WET) and secure wireless information transfer (WIT). Considering that the PS and transmitter belong to different service providers, we model this energy interaction through a Stackelberg game and propose an IRS-aided energy trading and secure communication (IRS-ETSC) scheme, in which the transmitter needs to pay an energy price as an incentive for the PS energy service. Specifically, the transmitter as the leader can control the energy price, WET time, two-stage phase shifts, and beamforming vector, while the PS as the follower can adjust the transmit power. To solve the non-convex leader game problem, we propose a two-step approach to decompose the original problem into two subproblems. The first subproblem can be solved independently by an efficient alternating optimization (AO) based algorithm, in which the closed-form optimal beamforming vector and energy phase shifts are alternately optimized. Then, the second subproblem is relaxed by semidefinite relaxation (SDR) and solved by an iterative algorithm based on block coordinate descent (BCD), where the optimal energy price, optimal WET time, and suboptimal information phase shifts can be obtained by golden section method and successive convex approximation (SCA) respectively. Both subproblems can converge to a stationary point. Numerical results show that compared with the traditional non-IRS scheme, the proposed IRS-ETSC scheme achieves utility improvement for both the PS and transmitter.

The tropical twilight of Daylight-Saving Time (DST): Enlightening energy savings from electricity markets across Brazilian regions

After more than three decades in a row, Brazil, the only equatorial country to adopt a DST policy, forsook it. The reduction delivered in electricity consumption, which was supposed to average out 4% per year, became ineffective and inefficient, because it focused mainly on lighting and residential consumers, rather than on cooling and the industrial sector. Moreover, the burden of reducing electricity consumption was only placed on the Southern regions of Brazil. The shutdown of this policy has raised questions about the country's commitment to its pledges on energy efficiency to the Paris Agreement. Based on data available from 2006 to 2017, this article compares the achievements of a command-and-control energy policy, such as DST, with those that would have arisen from an ETS (Energy Trading Scheme). The results show that, per year, society's net benefits might have been over 3 times greater: 12.58% rather than 4.1% for the reduction target of electricity consumption. Applying these targets to an ETS framework, net cost savings would be 104.09% rather than 33.64%, whereas mean electricity savings would make up 22.78% rather than 7.42%. Therefore, with ETS, mean electricity savings might turn out to be 5 times greater if compared with 4.1% savings reported yearly by DST. Furthermore, the Northern regions, which have historically been left out of DST policies, could have additionally earned up to € 16.8 billion per year from reducing their electricity consumption jointly with their Southern counterparties.

Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China

The Energy Consumption Guarantee Mechanism (ECGM) of renewable energy requires the prior consumption ratio of renewable energy, and regulates the proportion of renewable energy power in each province. The implementation of Renewable Portfolio Standard (RPS) will have a great impact on the energy portfolio management and trading strategy of electricity in the medium and long-term electricity market. Grid companies (or power exchange center), as the organizers of the medium and long-term market and the primary entities responsible for renewable energy consumption, need a set of scientific trading strategies for the overall scheduling of inter- and intra-provincial power exchange arrangements. Firstly, based on the medium and long-term power market trading rules of Jiangsu, China, this paper analyzes the risk of uncertainty in the power output of renewable energy, and introduces the optimization model into the cost of risk to schedule the consumption ratio of renewable energy in the province, namely arranging the amount of renewable and non-renewable energy electricity as well as that of electricity outside the province. Secondly, the risk levels and their response are determined, and the electricity arrangements inside and outside the province in the medium and long-term market are updated based on a time window rolling method and the proposed mechanism. Finally, illustrative cases are performed to verify the effectiveness of the model and to provide a reference for the organizers' decision-making of medium and long-term power exchange.

Enhanced-decision energy trading for island renewable microgrids considering large interruptible refrigeration load intra-hour switching

Large interruptible refrigeration load (LIRL) can be incorporated into the scheduling model of microgrid (MG) to optimize the operational cost. However, based on a classic 1-hour step, the LIRL is either turned on or off, resulting in unsatisfactory results. Although a smaller scheduling step can adjust the LIRI more finely, the computational burden will be increased simultaneously. To address the problem, this paper proposes a novel enhanced-decision energy trading strategy for renewable MGs. First, enhanced decisions are proposed, where on-state and off-state durations of LIRL in an hour instead of on or off state would be determined. On the basis, based on a 1-hour scheduling step, an enhanced-decision trading model is presented, where the energy operator maximizes the electricity sale profit, and load aggregators (LA) minimize the electricity usage cost. This optimization model is first converted into a classic mixed-integer quadratic programming (MIQP) problem, and then solved by a distributed method. Simulation studies indicate that the proposed energy trading strategy can reduce the electricity price fluctuation range by 0.43 yuan (20.6%), increase the social cost by 104 yuan (3.5%), and benefit the participators.

Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

Networked microgrids (MGs) have a great potential to improve the efficiency, reliability, resilience, security, and sustainability of power supply services. Peer-to-peer (P2P) energy trading built on a smart information system in networked MGs is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed and competitive energy trading market urges the need for an efficient energy trading strategy that incentivizes the self-interested MGs with various energy production and consumption profiles to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables the MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply, and energy trading in a dynamic manner, jointly considering the randomness of renewable energy generation and load demand, operational constraints of ESSs and transmission losses associated with energy exchange. The proposed energy control and bidding algorithm allows each MG to dynamically and independently determine its energy control actions and price-quantity bids/offers, while the proposed trading pair matching algorithm matches the MGs on a many-to-many basis with respect to their individual payoffs, which couple price-quantity bids/offers of the MGs with distance-dependent energy transmission losses associated with energy exchange. Numerical simulation results demonstrate that the proposed distributed energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while reducing energy transmission losses within the system.

Distributed Auction-Based Incentive Mechanism for Energy Trading Between Electric Vehicles and Mobile Charging Stations

With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first, we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides the winners without the knowledge of other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price’s resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme’s effectiveness compared with the baseline in the literature.

A distributed electricity energy trading strategy under energy shortage environment

This paper presents a power dispatch strategy combining the main grid and distributed generators based on aggregative game theory and the Cournot price mechanism. Such a dispatch strategy aims to increase the electricity under the power shortage situation. Under the proposed strategy, this paper designs a discrete-time algorithm fusing the estimation technique and the Digging method to solve the power shortage problem in a distributed way. The distributed algorithm can provide privacy protection and information safety and improve the power grid's extendibility. Moreover, the simulation results show that the proposed algorithm has favorable performance and effectiveness in the numerical example.

Congestion management under peer-to-peer energy trading scheme among microgrids through cooperative game

The peer-to-peer (P2P) energy trading among microgrids (MGs) can promote the renewable energy accommodation of network while maximizing the benefits of their own. However, unregulated trade behavior may cause network congestion which affect the safe and stable operation of the system. To this end, this paper proposes an effective congestion management method under P2P energy trading of MGs based on cooperative game strategy. For a congestion caused by several MGs, the Shapley value is firstly used to allocate the congestion marginal contribution. Then, a congestion price coefficient is optimally determined by minimizing the overall congestion cost while maximizing the overall revenue of all MGs, the MGs who are cost-sensitive are advised to make a change on their trading scheme. Taking a system composed of 4 MGs as an example, simulation study is finally conducted to validate the effectiveness and applicability of the proposed methods.

Blockchain-Enhanced Fair and Efficient Energy Trading in Industrial Internet of Things

As the technical support of the industrial Internet of Things, blockchain technology has been widely used in energy trading, data transactions, and Internet of Vehicles. However, most of the existing energy trading models only address the transaction security and transaction privacy issues that arise in the energy trading process, ignoring the fairness of resource allocation and transaction equity in the trading process. In order to tackle those problems, an energy trading scheme called HO-TRAD is proposed in this paper to improve the efficiency of model trading while ensuring the fairness of energy trading. We propose a new trading strategy in the HO-TRAD energy trading scheme that guarantees fairness in the allocation of trading resources by introducing an entity’s active reputation value. Use smart contracts to achieve transparency and ensure fairness in the transaction process. Based on the identity verification foundation of the consortium chain, the scheme enhances the existing PBFT consensus algorithm and improves the efficiency of model transactions. The experimental simulation indicates that the scheme requires less transaction time and has higher transaction fairness and security.

Aggregate regulation strategy of distributed energy storage under power spot market in China

The reform of power spot market in China provides a new profit mode, determining energy trading strategy based on the power spot prices for distributed energy storages. However, individually accessing every distributed energy storage to the dispatch centre results in a high cost and low efficiency, which needs to be improved by connecting through the aggregator. To this end, this paper proposes a regulation mode and strategy for distributed energy storages participating in energy trading through aggregation. First, the optimal centres of distributed energy storages are searched as the aggregation centres according to the electrical distance distributed by the energy storage, and the model of each distributed energy storage aggregation group is established. On this basis, the conditional value at risk (CVaR) method is introduced to quantify the income risk brought by the fluctuation of power spot prices, following with the optimization model for day-ahead stage and real-time stage. Finally, case studies under multiple scenarios of power spot market verify that the regulation mode and strategy can effectively guarantee the economic profits of distributed energy storages by setting aggregation groups and reasonable risk preference coefficients.

Blockchain for Transacting Energy and Carbon Allowance in Networked Microgrids

This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints.

Data-Driven Distributionally Robust Co-Optimization of P2P Energy Trading and Network Operation for Interconnected Microgrids

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account for the implementation practices, and the emerging soft open point (SOP) technology is used for the flexible connection of the multi-MGs. First, the energy management in individual MGs is modeled as a distributionally robust optimization (DRO) problem considering the P2P energy trading options and various operational constraints. Later, a novel decentralized and incentive-compatible pricing strategy is developed for P2P energy trading using the alternating direction method of multipliers (ADMM). Furthermore, the uncertainties in load consumption and renewable generation (RG) are taken into account and the Wasserstein metric (WM) is used to construct the ambiguity set of the uncertainty probability distributions (PDs). Consequently, only historical data is needed rather than prior knowledge about the actual PDs. Finally, the equivalent linear programming reformulations are derived for the DRO model to achieve computational tractability. Numerical tests on four interconnected MGs corroborate the advantages of the proposed P2P energy trading scheme and also demonstrate that the proposed DRO model is more effective in handling the uncertainties compared to the robust optimization (RO) and the stochastic programming (SP) models.

A consortium blockchain based energy trading scheme for Electric Vehicles in smart cities

The real time Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) energy trading have ameliorated the smart city environment. Electric Vehicles (EVs) in smart cities have made it possible to balance the energy demand and supply without burdening the power grids. They also help in minimizing both the heap amounts of pollution and the greenhouse gas emissions. EVs not only charge their batteries from Charging Stations (CSs), but when the smart city faces energy deficiency, they supply their surplus energy to the power grids to fulfill energy demand as well. In addition, EVs also balance the energy demand and supply through energy trading at the local level during peak hours, which strengthens V2V energy trading. However, some issues, such as security threats and privacy leakage prevent EVs from participating in the energy trading process. In addition, the lack of incentives and knowledge about the cost of reaching the energy trading place within minimum time are also big challenges. Therefore, to solve the aforementioned challenges, a secure and efficient scheme for V2V and V2G energy trading is proposed in this paper. The proposed scheme also helps in promoting environmental friendliness and making the smart cities sustainable and reliable. In the proposed scheme, energy trading transactions are secured using consortium blockchain, wherein the Local Aggregators (LAGs) are selected as authorized nodes. LAGs perform their role as energy brokers and are responsible for validating the energy trading requests using Proof of Authority (PoA) consensus mechanism. Moreover, a solution to find accurate distance with required expenses and time to reach the charging destination is also proposed, which effectively guides EVs to reach the relevant CSs and encourages energy trading. Besides, we propose a fair payment mechanism using a smart contract to avoid financial irregularities. An incentive provisioning mechanism is also given in the proposed work to prevent EVs from acting selfishly. The efficient power flow having minimized losses in the vehicular network is of much importance. Therefore, the energy losses incurred in both V2G and V2V are discussed in this work. Furthermore, Oyente is used for smart contract’s security analysis and for testing Ethereum’s resilience against different security flaws. Two attacker models are proposed and the security analyses of the models are also provided. The analyses show that the proposed system is robust against the double spending and Sybil attacks. Finally, the efficient performance of our proposed scheme is validated and analyzed. The simulation results proved that our proposed work outperforms existing work in terms of providing a secure and efficient energy trading platform for both V2G and V2V environments.

A multi‐objective optimization for planning of networked microgrid using a game theory for peer‐to‐peer energy trading scheme

In this paper, a multi-objective optimization technique is proposed for the planning of a networked microgrid based on peer-to-grid (P2G) and peer-to-peer (P2P) energy trading schemes. Two different criteria's including annual profit and energy index of reliability are taken into consideration to form a multi-objective function. The networked microgrid consists of three individual microgrids containing their own combinations of generation resources, batteries and residential loads. All microgrids are connected together and also to the main grid to meet the energy exchange requirements of P2P energy trading. A cooperative game theory technique based on a particle swarm optimization algorithm is used to model the networked microgrid, and to find the suitable sizes of the players that simultaneously maximize the payoff values of both objective functions. Besides, a comparative analysis is carried out for both P2G and P2P energy trading schemes. The results show that the outcomes are maximum when both criteria are considered in the optimization and P2P energy trading is carried out. The sensitivity analysis is performed on the selected parameters and verified the right change 0.003% and 4.5% in discount rate and electricity prices, respectively.

A mobile energy trading scheme based on Lightning Network

AbstractCurrent blockchain‐based energy trading models raise serious concerns regarding the high and capped transaction latency and expensive service charges. To solve these problems, In this article, we combine Lightning Network (LN) and smart contract to present a mobile energy trading scheme based on LN. The focal point of the scheme lies in transfer of value occurs off‐blockchain, which addresses the problem of transaction latency. Next, to solve the security of proposed scheme, we design a mechanism to delivery secret R. Participants exchange some parameters to calculate R instead of delivery it directly. The found security is guaranteed by committing funds into a multi‐signature address in proposed scheme. Then, we conduct a comprehensive experiment to evaluate the proposed scheme. The simulation and analysis results show that the proposed scheme is efficient than traditional trading. In addition, we analyze our entire scheme and conclude that it defenses anti‐tampering attacks and replay attack capacity effectively.

Privacy-Preserving Blockchain-Based Energy Trading Schemes for Electric Vehicles

An energy trading system is essential for the successful integration of Electric Vehicles (EVs) into the smart grid. In this paper, leveraging blockchain technology, we first propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. The CS2V scheme is useful in crowded cities where there is a need for a charging infrastructure that can charge many EVs daily. We also propose a privacy-preserving vehicle-to-vehicle (V2V) energy trading scheme. The V2V scheme is useful when charging stations are not available or far and cheaper prices can be offered from EVs, e.g., if they charge from renewable energy sources. In the V2V scheme, the privacy of both charging and discharging EVs including location, time, and amount of power are preserved. To preserve privacy in both schemes, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.

A Consortium Blockchain-Based Energy Trading for Demand Response Management in Vehicle-to-Grid

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals.