A Review of Incentive Based Demand Response Methods in Smart Electricity Grids

Alamaniotis, Miltiadis; Chrysikou, Vasiliki; Tsoukalas, Lefteri H.

doi:10.4018/ijmstr.2015100104

Cited by 17 publications

(6 citation statements)

References 45 publications

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“…Constraints ( 5) and ( 6) can be modified using the above-obtained equations. Thus, the value of π min t and π max t , in (9), can be replaced by Equations (11) and (12).…”

Section: Ementioning

confidence: 99%

“…The modeling of the IBDR program for the residential customers, including the loads of the customer to reduce the power consumption, are carried out in the intelligent environment, thereby reducing the customer bill. The IBDR models for handling DR problems, with optimization techniques such as the Stackelberg game, GAMS, CPLEX, and others, are used in SG to obtain a better result of demand reduction and cost minimization [7][8][9][10]. In recent years, game-theory-based algorithms have been implemented to solve the IBDR strategy bidding model from the SP and GO ends [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

et al. 2022

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Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

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“…Constraints ( 5) and ( 6) can be modified using the above-obtained equations. Thus, the value of π min t and π max t , in (9), can be replaced by Equations (11) and (12).…”

Section: Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

et al. 2022

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“…Its main benefits include maintaining the balance between supply and demand and deferring some of the required investments in electricity infrastructure [2]. However, for these schemes to successfully engage prosumers (producer and consumer) in abandoning some of their comfort and providing flexibility to the grid, the design of an attractive market structure that is beneficial to all stakeholders is paramount [3]. hand, IBDR have provisions for customers' individual preferences [9].…”

Section: Introductionmentioning

confidence: 99%

Applications of mechanism design in market-based demand-side management: A review

Abedrabboh

Al‐Fagih

2023

Renewable and Sustainable Energy Reviews

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“…A dynamically varying electricity price has given rise to the price directed electricity markets, where prices are driven by econometric models to encourage or discourage the electricity consumption (Saez-Gallego et al, 2016 ). In such markets, consumers must follow the prices 24/7 and make respective purchase decisions that reduce the expenses (Chrysikou et al, 2015 ). Though the idea sounds appealing, it is impossible for humans to non-stop monitor the market prices and decide accordingly.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids

Alamaniotis

2020

Front. Artif. Intell.

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This paper frames itself in an informational rich smart electricity grid where consumers have access to various streams of information and make decisions over their daily consumption pattern. In particular, a new intelligent management system to accommodate possible optimal decisions for elastic load consumption is discussed. The energy management system implements a fuzzy driven leaky bucket that manages the elastic load of a consumer by controlling the token rate buffer via a set of four fuzzy variables (among them the electricity price). The goal of this innovative system is to allow loads that are identified as elastic to be scheduled only when it is potentially beneficial to the consumer. To that end, a fuzzy algorithm comprised of a set of rules is developed to manage the token rate of the leaky bucket and through that the decisions over the fate of elastic loads. The developed system is applied on a set of real-world electricity consumption data taken from a residential consumer, and benchmarked against a full scheduling method, where the elastic load is fully scheduled offline. Results exhibit that the proposed fuzzy logic method outperforms the full scheduling method in the vast majority of the cases, i.e., over 79% of the cases with respect to consumption cost. Furthermore, they validate its ability to conduct real time decision making with no human in the loop.

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A Review of Incentive Based Demand Response Methods in Smart Electricity Grids

Cited by 17 publications

References 45 publications

Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Applications of mechanism design in market-based demand-side management: A review

Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids

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