Planning Model for Energy Routers Considering Peer-to-Peer Energy Transactions Among Multiple Distribution Networks

A high proportion of renewable energy affects the power quality of distribution networks,and surplus energy will be sold to the upstream grid at a low price.In this paper,considering peer-to-peer energy transactions,the energy router-based multiple distribution networks are analyzed to solve the above problems and realize collaborative consumption of renewable energy.Presently,the investing cost of an energy router is high,and research on the economic operation of energy routers in distribution networks is little.Therefore,this paper establishes a planning model for energy routers considering peer-to-peer energy transactions among distribution networks,and explores the benefits of peer-to-peer energy transactions through energy router based multiple distribution networks.A structure of an energy router suitable for peer-to-peer energy transactions is selected,and a power flow calculation model based on a multilayer structure is established.The energy router’s scheduling model is established,and unique functions of the energy router and revenue of each distribution network are considered.A power flow calculation model based on peer-to-peer interconnection of multiple distribution networks through energy routers is also established.Finally,simulation results verify the effectiveness of the proposed planning model.Results show that peer-topeer energy transaction among distribution networks through energy routers can effectively reduce the comprehensive cost of distribution networks,significantly improve the power quality of the distribution networks,and reduce the impact of power fluctuation on the upstream grid incurred by the distribution network.

Calculation Model and Allocation Strategy of Network Usage Charge for Peer-to-peer and Community-based Energy Transaction Market

The emergence of prosumers in distribution systems has enabled competitive electricity markets to transition from traditional hierarchical structures to more decentralized models such as peer-to-peer(P2P)and community-based(CB)energy transaction markets.However,the network usage charge(NUC)that prosumers pay to the electric power utility for network services is not adjusted to suit these energy transactions,which causes a reduction in revenue streams of the utility.In this study,we propose an NUC calculation method for P2P and CB transactions to address holistically economic and technical issues in transactive energy markets and distribution system operations,respectively.Based on the Nash bargaining(NB)theory,we formulate an NB problem for P2P and CB transactions to solve the conflicts of interest among prosumers,where the problem is further decomposed into two convex subproblems of social welfare maximization and payment bargaining.We then build the NUC calculation model by coupling the NB model and AC optimal power flow model.We also employ the Shapley value to allocate the NUC to consumers fairly for the NUC model of CB transactions.Finally,numerical studies on IEEE 15-bus and 123-bus distribution systems demonstrate the effectiveness of the proposed NUC calculation method for P2P and CB transactions.

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study.In this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg game.Operator holds a large-scale ESS that is shared among users in the form of energy transactions.It sells energy to users and sets the selling price first.It maximizes its profit through optimal pricing and ESS dispatching.Users purchase some energy from operator for the reduction of their demand charges after operator's selling price is announced.This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model.The upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'costs.The bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing techniques.Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

A Distributed Transactive Energy Mechanism for Integrating PV and Storage Prosumers in Market Operation

The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the potential roll-out of home energy management systems(HEMSs),which provide a means for prosumers to respond to externalities such as energy price,weather,and energy demands.However,the economic operation of prosumers can affect grid security,especially when energy prices are extremely low or high.Therefore,it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely,the network operator,prosumer,and aggregator.In this paper,a novel transactive energy(TE)-based operational framework is proposed.Under this frame-work,aggregators interact with the distribution grid operator through a negotiation process to ensure network security,while at the lower level,prosumers submit their schedule to the aggregator through the HEMS.If network security is at risk,aggregators will send an additional price component representing the cost of security(CoS)to the prosumer to stimulate further response.The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

Transactive Energy and Flexibility Provision in Multi-microgrids Using Stackelberg Game

Aggregating demand side flexibility is essential to complementing the inflexible and variable renewable energy supply in achieving low carbon energy systems.Sources of demand side flexibility,e.g.,dispatchable generators,storage,and flexible loads,can be structured in a form of microgrids and collectively provided to utility grids through transactive energy in local energy markets.This paper proposes a framework of local energy markets to manage this transactive energy and facilitate the flexibility provision.The distribution system operator aims to achieve local energy balance by scheduling the operation of multi-microgrids and determining the imbalance prices.Multiple microgrid traders aim to maximize profits for their prosumers through dispatching flexibility sources and participating in localised energy trading.The decision making and interactions between a distribution system operator and multiple microgrid traders are formulated as the Stackelberg game-theoretic problem.Case studies using the IEEE 69-bus distribution system demonstrate the effectiveness of the developed model in terms of facilitating local energy balance and reducing dependency on the utility grids.

From demand response to transactive energy: state of the art

This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed.Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making.The transition from demand response to transactive energy is also discussed.

Security Constrained Decentralized Peer-to-Peer Transactive Energy Trading in Distribution Systems

Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.

Blockchain-based Peer-to-Peer Transactive Energy System for Community Microgrid with Demand Response Management

Interest in transactive energy frameworks(TEFs)is proliferating due to the modern smart grid paradigm.This paper proposes a TEF,which applies auction-theory,incorporates a system of agents,and facilitates a transactive energy market(TEM)through an auctioneer.Further,it also enables peer-to-peer(P2P)energy trading among the residential buildings in community microgrid for possible monetary benefits.In this framework,there are three agents,namely,auctioneer,participants,and utility.The auctioneer is a managing agent modeled using auction theory to determine day-ahead internal market-clearing price and quantity.The participants are autonomous and rational decision-makers;they aim to minimize their electricity bills through the demand response(DR)management.Two types of architectures,one with the third-party agent demonstrated using the MATLAB environment and the other with the virtual agent(without third-party)implemented using the blockchain environment are presented.The simulation results reflect significant monetary benefits to each market participant,improved community selfsufficiency,self-consumption,and reduced reliance on the utility grid.

Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

In recent years,the advent of microgrids with numerous renewable energy sources has created some fundamental challenges in the control,coordination,and management of energy trading between microgrids and the power grid.To respond to these challenges,some techniques such as the transactive energy(TE)technology are proposed to control energy sharing.Therefore,this paper uses TE technology for energy exchange control among the microgrids,and applies three operation cases for analyzing the energy trading control of four and ten microgrids with the aim of minimizing the energy cost of each microgrid,respectively.In this regard,Monte Carlo simulation and fast forward selection(FFS)methods are respectively exerted for scenario generation and reduction in uncertainty modeling process.The first case is assumed that all microgrids can only receive energy from the network and do not have any connection with each other.In order to maximize the energy cost saving of each microgrid,the second case is proposed to provide a positive percentage of cost saving for microgrids.All microgrids can also trade energy with each other to get the most benefit by reducing the dependency on the main grid.The third case is similar to the second case,but its target is to indicate the scalability of the models based on the proposed TE technology by considering ten commercial microgrids.Finally,the simulation results indicate that microgrids can achieve the positive amount of cost saving in the second and third cases.In addition,the total energy cost of microgrids has been reduced in comparison with the first case.

Transactive Energy Systems in Active Distribution Networks:A Comprehensive Review

Increasing penetration of distributed energy resources(DERs)introduced by different stakeholders,poses an immense challenge to power network operators.The traditional direct control of local DERs has the risk of violating preferences and privacies of stakeholders.A promising solution for supplydemand coordination is to utilize a transactive energy(TE)based energy management method to indirectly coordinate the local DERs,which enables the distribution-level energy providers,consumers,and prosumers to trade energy with each other through a transactive energy system(TES)trading platform.This paper provides a comprehensive review of a TES and presents a detailed classification from different perspectives,including TES participants,structure,commodity,clearing method,and solution algorithm.The presented detailed component-scale classification can be used as a reference for future TES designs.Finally,two additional market tools,i.e.,penalty mechanism and loss allocation mechanism,are discussed as future focus areas,which can be seen as necessary complements to a TES for ensuring feasibility and fairness of energy trading.

Reinforcement learning-driven local transactive energy market fordistributed energy resources

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.