Dynamic Demand Response Using Customer Coupons Considering Multiple Load Aggregators to Simultaneously Achieve Efficiency and Fairness

Li, Zhechao; Wang, Shaorong; Zheng, Xuejun; León, Francisco de; Hong, Tianqi

doi:10.1109/tsg.2016.2627140

Cited by 43 publications

(33 citation statements)

References 20 publications

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“…(1) Full peer-to-peer energy trading, where market players directly trade energy with each other without mediators [62,63]. (2) Energy trading through a mediator, where a mediator participates in the LEM on behalf of buyers and sellers [38]. (3) Hybrid energy trading, where buyers and sellers can trade energy either directly or through a mediator [14].…”

Section: Local Energy Markets At Power Distribution Levelmentioning

confidence: 99%

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Georgilakis

2020

Energies

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The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operation of local energy markets, classifying and analyzing current and future research directions in this area.

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Section: Local Energy Markets At Power Distribution Levelmentioning

confidence: 99%

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Georgilakis

2020

Energies

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“…P in,max c,t and P out,max c,t denote the upper bound of load transferred in and out, respectively. Equation (8) guarantees that the total amount of the shiftable loads remain unchanged in the operation horizon. P IL,max c,t is the upper bound of the interruptible load.…”

Section: The Model Of Lamentioning

confidence: 99%

“…Recently, enormous interest has grown in exploring different strategies to improve the benefits of participants in the future electricity market [6][7][8][9][10][11][12][13]. For an LA of residential consumers, it can reduce electricity cost by shifting the load from peak hours to off-peak hours or purchasing from other electricity suppliers when the time-of-use (TOU) tariffs of the corresponding DNO are at the peak [6][7][8]. Furthermore, the LA can also benefit from providing ancillary services to power systems such as frequency regulation service [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed Peer-to-Peer Electricity Trading Considering Network Loss in a Distribution System

Jin

Zheng

et al. 2019

Energies

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In this paper, a distributed peer-to-peer (P2P) electricity trading model was proposed to study economic interactions between load aggregators (LAs) and microgrid operators (MGOs) considering network losses in a distribution system. In this model, the economic interactions among market participants were formulated as a Nash bargaining game, where LAs and MGOs can bargain with each other on the trading volume of electricity and payment. To achieve the Nash bargaining solution, the game was divided into two sub-problems: social welfare maximization and payment bargaining. Then, the alternating direction method of multipliers was used to solve the two sub-problems with limited information exchange. Finally, we tested the proposed model on a 12 × 12 km2 distribution system, and the results verify its effectiveness.

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“…Aggregators serve two important purposes. (1) they provide optimal demand side management (DSM) services to DSOs, resulting in a financial bonus from the DSO, and (2) they ensure a reduction in the electricity bill of end users, which encourages them to participate actively in DR programs [4,5]. As renewable and alternative energy sources become more affordable, it is becoming more cost-effective to combine them with conventional methods of energy supply [6].…”

Section: Introductionmentioning

confidence: 99%

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Essiet

Sun

2020

Applied Sciences

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This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

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Dynamic Demand Response Using Customer Coupons Considering Multiple Load Aggregators to Simultaneously Achieve Efficiency and Fairness

Cited by 43 publications

References 20 publications

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Distributed Peer-to-Peer Electricity Trading Considering Network Loss in a Distribution System

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

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