A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers

Nizami, Mohammad Sohrab Hasan; Hossain, M. J.; Mahmud, Khizir

doi:10.1109/tsg.2020.3011192

Cited by 30 publications

(12 citation statements)

References 25 publications

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“…Author in Reference [19,20] proposed EMS based on TE market model for scheduling of residential buildings load in day-ahead market to minimize the cost and maximize profit of the consumer in real-time market. In References [21,22].…”

Section: Application Areas Of Transactive Energymentioning

confidence: 99%

Transactive energy management for microgrids considering techno‐economic perspectives of utility – a review

Kumar

Jain

Sharma

2022

Intl J of Energy Research

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In a power system, distributed energy resources, including renewables, are advantageous for reducing power transmission losses and environmental pollution. However, increasing penetration of distributed renewable energy sources is challenging as they may pose security issues to the grid and centralized control of quantum number of such sources is quite impossible. Microgrid based distribution system is facilitating integration of distributed renewable energy sources as they can supply the local loads and reduce burden on the grid. Moreover, distributed control mechanism of different microgrids enhances the functioning efficiency of the grid. However, intermittent and uncertain nature of renewables leads into demand-supply imbalance in a microgrid. Networked microgrids is new concept where the energy sharing between the microgrids may encourage renewable integration and reduce demand-supply imbalance. In order to manage such energy sharing among the different components in a microgrid or different microgrids in a networked structure, an appropriate energy management approach is necessitated. Transactive energy is an emerging technology for energy management in the distribution system, which uses market mechanisms for energy management. This paper discusses various types of market structures and market clearing approaches in transactive energy framework for energy management. Moreover, techno-economic perspectives of utility in transactive energy trading, such as network constraints and network usage charges, are elaborated.Finally, this review details the challenges and future scope in the transactive energy management of microgrids and networked microgrids.

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Section: Application Areas Of Transactive Energymentioning

confidence: 99%

Transactive energy management for microgrids considering techno‐economic perspectives of utility – a review

Kumar

Jain

Sharma

2022

Intl J of Energy Research

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“…Once demand offers are quantified per each customer, the aggregators' offers can be then found, as given in (24).…”

Section: Aggregators: Demand Offersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Advanced TES models were proposed in [21][22][23][24][25][26][27][28][29][30] to cater for the constraints of distribution networks. This was done either by the definition of power thresholds to the aggregate net-demand [21][22][23][24][25] or by using Optimal Power Flow (OPF) [26][27][28][29][30] as the decision-making algorithm. For instance, the price-based OPF presented in [26] aims to solve congestions through the provision of incentive signals to customers in return of controlling their power consumption for the benefits of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

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Transactive Energy System for Distribution Network Management: Procuring Residential Flexibility Services Under Dynamic Pricing

et al. 2022

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The formulation of dynamic pricing is one of the emerging solutions to guide residential demand for the benefits of the bulk power system. However, the schedule of residential demand in response to time-differentiated energy prices could cause congestions in distribution networks at both the lowest-price and highest-price time intervals. To enable the adoption of dynamic pricing, this work presents a novel framework to manage the constraints of distribution networks based on the concept of Transactive Energy System (TES). The TES-based framework produces incentives during network issues to unlock customers' flexibility services to reschedule controllable assets (e.g., batteries). By running Home Energy Management Systems (HEMS), the flexibility of customers to modify schedules are quantified against predefined set of incentives. For each incentive, the amounts of net-demand change per customer are aggregated and submitted through aggregators to the Distribution System Operator (DSO) in the forms of both generation offers (reducing demand) and demand offers (increasing demand). The latter are crucial to cater for generationdriven network issues. The resulting aggregators' staircase bidding curves are embedded to an advanced Optimal Power Flow (OPF) model to identify the successful offers to manage network constraints whilst minimizing incentives paid to aggregators. This allows defining incentives and quantities directly without extensive iterations between DSO and aggregators. The application of the framework to an urban 11kV feeder shows its effectiveness to manage congestions. However, the highly variations in dynamic prices increase the amounts of incentives particularly when flexibility services are requested at evening and night time intervals.INDEX TERMS Aggregators, dynamic pricing, flexibility, home energy management systems, network management system, transactive energy system.

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“…BACKGROUND D UE to the increase in the utilization of renewable energies as variable and non-dispatchable resources, the power system is facing new problems related to the balance between demand and generation [1]. In this regard, new resources for providing the required balance management and other flexibility services are needed [2]. End-users can provide energy flexibility for the power system due to their ability to modify their consumption [3].…”

Section: Introductionmentioning

confidence: 99%

Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading

et al. 2021

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The potential of end-users to modify their consumption pattern makes them an interesting resource for providing energy flexibility in energy communities. Thus, active end-users require sufficient incentives and automated trading and management schemes. In order to enable increased small-scale endusers participation for flexibility service provision, a new design for flexibility trading is required to model the behavior of different agents and their interactions in energy communities. The novelty of our work lies in proposing an iterative game-based approach in which all agents -consisting of the distribution system operator (DSO), aggregators, and customers-can determine their decision variables to optimize their own objective functions and interact with others to modify their decisions according to others' decisions. In addition, three scenarios are considered to study the effects of agents' freedom while setting their decision variables (by removing one of their constraints in their corresponding decision-making problem). Moreover, the impact of the presence of interruptible loads in comparison with shiftable loads is investigated in this paper. According to the simulation results, it is found that in the scenario where end-users have fewer constraints, in presence of interruptible loads, end-users gain greater income compared to the absence of interruptible loads.INDEX TERMS Aggregators, energy community, flexibility management, game-based approach, local energy trading.

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A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers

Cited by 30 publications

References 25 publications

Transactive energy management for microgrids considering techno‐economic perspectives of utility – a review

Transactive energy management for microgrids considering techno‐economic perspectives of utility – a review

Transactive Energy System for Distribution Network Management: Procuring Residential Flexibility Services Under Dynamic Pricing

Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading

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