Transactive Market Research Articles

Virtual Prosumers’ P2P Transaction Based Distribution Network Expansion Planning

The deployment of prosumers with distributed energy resources (DERs) has facilitated the prosumer-centric transactive market, giving rise to the consequent necessity for the coordination between prosumers and the distribution networks. This paper proposes an incentive-based expansion planning method considering virtual peer-to-peer (P2P) transaction among prosumers, which is characterized as a tri-level planning-operation-transaction framework. Each prosumer optimizes its internal response of DERs and external P2P transaction jointly, formulating virtual P2P transaction. Under such a paradigm, the distribution company (DISCO) identifies the optimal expansion planning in the network assets, while the independent distribution system operator (IDSO) is responsible for the optimal operation of the expanded distribution networks. To derive appropriate incentives for each prosumer, the DISCO and IDSO allocate long-term investment network charges and short-term operation network charges on prosumers, along with the designed financial transmission rights coupling multiple time scales. Through such incentives, self-profit driven virtual transactions can relieve operation problems and postpone expansion planning, acting as a short-term alternative. Moreover, a risk-based approach is proposed for prosumer to flexibly change strategy according to different risk preference. Finally, results from the case study demonstrate the superiority and effectiveness of the proposed method in the collaborative application of virtual transaction in the expansion planning.

Hierarchical Transactive Energy Scheduling of Electric Vehicle Charging Stations in Constrained Power Distribution and Transportation Networks

There is a growing opportunity to explore transactive energy potentials among electric vehicle charging station (EVCS) systems as the ongoing trend is toward the deployment of more distributed energy resources such as solar PV and energy storage units at EVCS premises. In this paper, we propose a multi-agent hierarchical framework for energy scheduling and trading of EVCSs considering the mobility constraints in the transportation network (TN) and the operational constraints in the power distribution network (PDN). Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on a multi-period traffic assignment problem (TAP) solved by the traffic operator (TO) agent. A modified single-sided auction mechanism with limited shared information is used to clear the electricity market based on submitted EVCS bids and offers. The resulting trading operations are shared with the PDN operator to guarantee a reliable network operation. A trading adjustment signal is sent back to market participants (i.e., EVCSs) in case of any PDN violations. We use a realistic three-phase unbalanced representation formulated as a MISOCP problem to model the PDN operation. A four-stage solution method is proposed to solve the proposed EVCS energy scheduling and trading problem. Numerical simulations performed on a modified IEEE 33-bus test system and 12-node benchmark TN prove the effectiveness of the proposed multi-agent-based hierarchical transactive market model and its solution approach for EVCSs.

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

Historical-based bidding strategy in transactive energy market for household: Data driven analysis

The energy market design is changing to enable distribution-level transactions in smart cities, encouraging end-users to participate efficiently in the market. The Transactive Energy (TE) concept opens the floor for a new market settlement philosophy that can benefit the end-user and Distributed Generators (DGs). The bidding of the customers in the distribution level market raises many concerns in the latest reports due to many factors (e.g., lack of information about the participant’s flexibility). This paper proposes a non-intrusive bidding strategy to enhance the accuracy of the household’s participation in the transactive electricity market. The proposed model utilizes the historical household user energy consumption to build the thermal model and develop the bidding strategy. The proposed approach examines the bidding in the context of user preferences of the total consumption, time at bidding, day of bidding, and ambient temperature. The results prove that the proposed strategy can significantly improve the flexibility’s recognition of the customers’ participation in the transactive market. Finally, the efficacy of the bidding method is examined via numerical analysis of actual data.

Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV-, Wind-, and Biomass-Based Resources

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

A network-secure peer-to-peer trading framework for electricity-carbon integrated market among local prosumers

The emergence of prosumers with considerable renewables promotes the lower-carbon operation of distribution network. However, prosumers’ flexible roles also bring new challenges to establish a fairness distribution-level transactive market and ensure the security in distribution network. To address this issue, this paper presents a network-secure peer-to-peer (P2P) market with two-stage framework. In the first stage, a multi leaders multi followers (MLMF) Stackelberg game is utilized to establish the P2P electricity-carbon integrated market, which considers stakeholders’ changeable roles between producer and consumer in the energy and carbon allowance transaction. The existence and uniqueness of the market equilibrium are also verified. In the second stage, network reconfiguration is taken into consideration to support the transactive results and avoid the restriction on node injected power to the largest extent. Numerical results show that the established P2P electricity-carbon integrated market can reduce carbon emissions by over 6% without harming prosumers’ total revenues.

Competitive Equilibriums of Multi-Agent Systems over an Infinite Horizon

In this paper, we investigate the properties of competitive equilibriums for dynamic multi-agent systems (MAS) over an infinite horizon. When there is no external resource supply, a group of dynamic agents with distributed resource allocations form a transactive market to share their resources at a specific price. Each agent makes decisions on the locally consumed resource and the traded resource. The system reaches a competitive equilibrium when each agent's payoff is maximized and the tradings are balanced. Firstly, we consider general utility functions and show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Secondly, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS for which the system-level social welfare optimization becomes a classical constrained linear quadratic regulator (CLQR) problem. We construct explicitly a feasible set of initial conditions under which the price under a competitive equilibrium is zero for all time.

Transactive charging management of electric vehicles in office buildings: A distributionally robust chance-constrained approach

In this paper, a new energy management model is proposed to determine the optimal scheduling of an office building which includes electric vehicle (EV) charging piles, batteries, and rooftop photovoltaic systems. To optimally manage the electricity procurement of the building and mitigate the rate of transformer aging, the building energy management system (BEMS) employs the flexibility of batteries and EV charging. In the proposed model, to incentivize EV owners to offer their flexibility, the BEMS organizes a transactive market among plugged-in EVs. To this end, EV owners submit their response curves and the target state-of-charge to the BEMS. Then, the transactive market is cleared to determine the market-clearing price for each EV, the optimal EV charging decisions, and accordingly, the scheduling of office building. Also, to model the correlated uncertainties of solar power generation and demand, the distributionally robust chance-constrained method is employed. Moreover, the “Big-M” technique and the piecewise linear approximation method are utilized to linearize the optimization problem. Finally, the case of a building with 100 charging piles is studied. The numerical results illustrate a decrease in the total operating cost of BEMS and the rate of transformer aging compared to uncontrolled charging and direct control approaches. • Developing a TE model to incentivize EVs’ participation in the energy management program. • Developing a new response curve model for EV owners. • Incorporating the transformer’s loss of life in the proposed TE model. • Developing a distributionally robust chance-constrained building energy management model. • Linearizing non-linear equations to mitigate the computational complexity.

The flexible roles of distributed energy storages in peer-to-peer transactive energy market: A state-of-the-art review

With the development of distributed energy resources, the peer-to-peer (P2P) transactive energy market has emerged as an effective method for consuming the excess renewable energy on the end-user side. In the current research on transactive markets, distributed energy storages (DESs) have been widely applied in energy trading for transaction flexibility and system management. These small-scale energy storages have achieved a considerable momentum that, if properly harnessed, may unleash their potential for prosumers’ utility maximization, reserves for renewable energy uncertainty, and transmission congestion relief. The present work reviews distributed energy storage in the transactive market, classifying and analyzing 120 papers according to their applications, algorithms, and adopted policies. This study first identifies DES functions in wholesale and transactive markets and then provides the mathematical models of DESs in various transactive market types in detail. This paper then focuses on the pricing mechanism for the DESs’ contributions in the P2P transactive energy market. A transactive energy market mechanism for accommodating DESs is proposed based on the Vickrey–Clarke–Groves framework to meet incentive compatibility. The energy policies related to the promotion of DESs in electricity markets are listed and compared. Furthermore, this review highlights the possible questions and future development opportunities according to the analyzed studies. With the roles and relationships among aggregators, DES owners, and power grid determined, the benefit of participating P2P transactive market can be further improved.

Distributed Transactive Coordination of Residential Communities Aiming at Fulfilling Households’ Preferences

Transactive energy (TE) provides joint market and control functionality to manage distributed energy resources (DERs) in distribution networks. This work develops a real-time TE management framework that allows residential customers to actively join in the real-time transactive market with fulfilling households’ preferences including comfort, economical energy consumption, and privacy-preserving. In this regard, first, a user-friendly algorithm is developed to calculate the real-time willingness to pay (bid) for electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) units considering customers’ preferences and concerns. Then, to preserve the privacy of households, the centralized market-clearing problem to maximize social welfare is decomposed into several subproblems using the alternating direction method of multipliers (ADMM) approach. Also, closed-form solutions to all subproblems are derived to simplify implementation and mitigate the computational complexity instead of solving optimization subproblems directly. This model is then implemented in a case study with several numbers of smart homes. The numerical results illustrate that the proposed distributed transactive model not only satisfies households’ comfort preferences but also decreases the average charging cost of EV batteries by 40% compared to the uncontrolled charging model. Further, the results show that our proposed model significantly mitigates the computational burden of the transactive market clearing problem compared to the centralized approach and the distributed approach without closed-form solutions.

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.

Designing Transactive Market for Combined Heat and Power Management in Energy Hubs

This paper introduces a transactive market design for a combined heat and power (CHP) based energy hub (hub). The proposed model allows a hub operator to supply the hub’s demands by participating in the day-ahead market and a transactive market with CHPs and also in the real-time market by using a recursive moving window algorithm. The proposed local energy market for a hub operator and CHPs is based on the double auction P2P trading mechanism. The model develops an optimal bidding and offering strategies for CHPs and hub operators, respectively, to achieve optimal transactions. The CHPs may be equipped with boiler unit and heat buffer tank (HBT) beside CHP units. The uncertain nature of the hub’s electrical load, real-time and day-ahead markets prices and wind speed is addressed by using robust optimization. The procedure aimed at minimizing the worst-case CHP-based hub’s demand procurement cost even though flexibly regulating the solution robustness. Further, case studies investigate the economic impact of robustness on the hub’s cost.

Transactive Energy Market Mechanism With Loss Implication

Electric distribution network is undergoing fundamental transition from conventional top-down structure to an environment where local prosumers, as transactive agents, are able to form peer-to-peer (P2P) energy trades among themselves. One of the challenges in such networks is allocating the incurred losses to the peers of the transactive market through a transparent and fair framework. In this article, it is demonstrated that the existing methods of loss allocation may cause a cross-subsidization problem when it comes to transactive energy systems, and a solution is offered to the problem. Furthermore, a novel transactive market mechanism is proposed which deploys the loss allocated to each transaction as a criterion to match the peers of the market for energy trading. It will be shown that the presented mechanism will help in diminishing the cross-subsidization problem. More importantly, our method will encourage the agents of the transactive market to trade energy with others located at a shorter distance, rather than farther ones in the network. This is indeed a salient goal of the smart grid concept and will give rise to prominent technical and administrative benefits regarding the transactive energy market and its players.

Designing a Transactive Framework for Future Distribution Systems

This article introduces a novel transactive energy management mechanism for future distribution systems with private microgrids. A third-party agent based on a transactive energy trading mechanism is designed which provides secure information sharing between participants. The proposed third-part agent acts as an independent economic entity (IEE) and clears the local energy market between the microgrid and distribution system. IEE determines the transactions corresponding to the maximum social welfare based on participants' submitted economic values. The economic values of entities are bids for buying and offers for selling the power. Two different pricing strategies for the approved transactions are presented. Distributed optimization for the self-scheduling based on an alternating direction method of multipliers has been implemented. Distributed optimization in a rolling fashion satisfies equality of complicated voltage magnitude, and voltage angles constraints at the connection buses. Furthermore, the participation of the distribution system in the day-ahead and real-time markets besides the transactive market is studied. The proposed structure is evaluated on a test distribution system with three microgrids in terms of energy trading analysis, and operation scheduling. COMP: Please remove colon appearing after table number in all the table captions.

Realistic energy commitments in peer-to-peer transactive market with risk adjusted prosumer welfare maximization

As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modelled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be less.

Optimal Management of Transactive Distribution Electricity Markets With Co-Optimized Bidirectional Energy and Ancillary Service Exchanges

This paper develops a novel co-optimized distribution system management algorithm for the distribution system operator to optimally manage the ever-increasing penetration of networked microgrids that are participating in the transactive distribution market as prosumers. For the first time, we evaluate how the incorporation of joint bidirectional energy and ancillary service exchanges could quantitatively increase the operation economy, flexibility, and reliability for both the microgrids and the distribution grid. We formulate the joint distribution system management problem using a bilevel programming approach, in which the upper level is a distribution system optimal management problem for the distribution system operator and the lower level is a microgrid optimal scheduling problem for each participating microgrid operator. Uncertainties from renewable energy generation and responsive loads within the distribution system are also incorporated in the problem formulation. The bilevel stochastic programming model is then reformulated as a single mixed-integer model to solve. The simulation-based case study supports that the proposed management scheme is capable of enhancing energy independency, system-wide efficiency, operational reliability, and economy of the distribution system in comparison with conventional transactive market management schemes.

Improving Grid Network Operations Through an Improved Energy Market

Distributed power generation can lead to overvoltage conditions, potentially damaging devices connected to the electrical grid. A method to improve the management of distributed power is presented to limit damaging overvoltage conditions using a transactive energy market and a Tabu Ant Colony Optimizer to manage activity on the distribution network. Overvoltage situations representing the intermittent power contributions from distributed sources were modeled on this network, and the results of current control methods were compared to those obtained using the developed Tabu Ant Colony Optimizer. Existing real time control methods resulted in overvoltages exceeding six percent above nominal, while the Tabu Ant Colony Optimizer resulted in overvoltage of less than three percent. Using this automated energy market mechanism as a management layer for the distribution network demonstrates how computational intelligence approaches can assist engineering managers responsible for these complex systems.

Multiagent-Based Transactive Energy Management Systems for Residential Buildings With Distributed Energy Resources

Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15–20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices.

A Transactive Energy Framework for Coordinated Energy Management of Networked Microgrids With Distributionally Robust Optimization

Networked microgrids (MGs) are considered as an emerging grid design for the future distribution system (DS). The coordination of the networked MGs is critical in order to further enhance the operation efficiency and reliability of the system. In this paper, a transactive energy (TE) framework is proposed for the coordinated energy management of networked MGs in DS. Instead of direct coordination signals and fixed pricing schemes, the distribution network operator (DNO) organizes a transactive market with the MGs to coordinate the energy management in the operation. Further, a distributionally robust optimization (DRO)-based algorithm is developed to provide a robust solution of the detailed scheduling decisions in the proposed TE framework under uncertainty without being too conservative. Case studies with the proposed framework were conducted with the IEEE 33-bus system with three MGs and the IEEE 123-bus system with nine MGs. The results of the case studies show that the proposed TE-based framework can effectively coordinate the energy scheduling of the MGs. The operational cost of the DS is reduced significantly. Meanwhile, the proposed DRO-based algorithm provides a robust but not over-conservative solution for the operation decisions of the DNO and MGs in the proposed framework.

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

This paper proposes a distributed multienergy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas, and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multimicrogrid problems.