A market-based framework for demand side flexibility scheduling and dispatching

Torbaghan, Shahab Shariat; Blaauwbroek, Niels; Kuiken, Dirk; Gibescu, Madeleine; Hajighasemi, Maryam; Nguyen, Phuong H.; Smit, Gerard J. M.; Roggenkamp, Martha; Hurink, Johann L.

doi:10.1016/j.segan.2018.03.003

Cited by 68 publications

(37 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most recent works include battery aggregated operation in distribution grids [13]- [20]. [13] presents an analysis of operating central storage units directly connected to mediumvoltage grids to provide power system services.…”

Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

“…Though, augmented Lagrangian methods facilitate to find global optimal solutions in a reasonable computational time in a distributed manner. Finally, [20] presents a market based flexibility exchange framework for multiple aggregators competing to solve the same congestion problem. It provides basis to formulate algorithms that are capable to deal with multiple aggregators and the work presented in this paper answers the question regarding re-scheduling FDs where an aggregator needs to compromise by activating a certain flexibility volume.…”

Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Olivella‐Rosell

Rul⋅lan

Lloret-Gallego

et al. 2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.

show abstract

Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

Section: B Aggregated Flexibility Servicesmentioning

confidence: 99%

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Olivella‐Rosell

Rul⋅lan

Lloret-Gallego

et al. 2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…In recent years, literature exhibited a new generation of energy management systems (EMS) approaches for MGs, which aim at dealing with the management of energy in variable operating and technological contexts. However, power flow control and the guarantee of highly reliable and stable MGs are becoming progressively complex [1]. Optimizing the size of the components and adopting an EMS strategy are essential to decreasing the cost of the system and limiting its negative effects [2].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures

2020

View full text Add to dashboard Cite

This paper presents both an extensive literature review and a qualitative and quantitative study conducted on nearly 200 publications from the last six years (based on international experience and a top-down analysis framework with five classification levels) to establish the main trends in the field of centralized energy management systems (EMS) for microgrids. No systematic trend analyses have been observed in this field in previous literature reviews. EMS attributes for several features such as objective functions, resolution techniques, operating models, integration of uncertainties, optimization horizons, and modeling detail levels are considered for main trend identification. The main contribution of this study is the identification of four specific existing research trends: (i) dealing with uncertainties (comprises 33% of the references), (ii) multi-objective strategy (29%), (iii) traditional paradigm (21%), and (iv) P-Q challenge (17%). Each trend is described and analyzed based on the main drive of these separate research fields. The key challenges and the way to cope with them are described based on the rationality of each trend, the results of previous reviews, and the previous experience of the authors. Overall, finding these main trends, together with a complete paper database and their features, serve as a useful outcome for a better understanding of the current research-specific challenges, opportunities, potential barriers, and open questions regarding the creation of future centralized EMS developments. The traditional numerical analysis is insufficient to identify research trends. Therefore, the need of further analyses based on the clustering approach is emphasized.

show abstract

“…An incentive-based residential DR program was proposed in [33] to aggregate the loads to meet a pre-defined load profile. A load aggregator was also introduced in a day-head market for various objectives [34,35]. Game theories were applied in demand response applications [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

et al. 2019

View full text Add to dashboard Cite

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.

show abstract

A market-based framework for demand side flexibility scheduling and dispatching

Cited by 68 publications

References 44 publications

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures

Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing

Contact Info

Product

Resources

About