Distributed Optimization for Scheduling Electrical Demand in Complex City Districts

Diekerhof, Michael; Schwarz, Sebastian; Martin, Florian; Monti, Antonello

doi:10.1109/jsyst.2017.2713798

Cited by 14 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply the distributed exchange ADMM algorithm defined in ( 15)- (20) to the equivalent optimal exchange problem in ( 10)-( 13), where we assign every ADMM subproblem in (15), i.e., every prosumer local-level optimization problem, to a separate computing process on a compute cluster. Motivated by earlier work in [24], we consider a residential DR peak shaving application that is intended to be both a suitable and a sufficiently general test case for our analyses. This means that nonfundamental variations in the assumptions and asset configurations do not restrict and affect the generality of the presented results and findings.…”

Section: Admm Computational Performance Study For a Dr Peak Shaving A...mentioning

confidence: 99%

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Schwarz

Monti

2023

IEEE Systems Journal

View full text Add to dashboard Cite

This article quantifies and benchmarks the computational performance of the distributed alternating direction method of multipliers (ADMM) optimization algorithm applied to a slowdynamics demand response peak shaving application. We propose a hierarchical demand response architecture, in which a commercial aggregator acts as the supplier and system-level flexibility service provider to a portfolio of residential prosumers. The prosumers are equipped with flexible distributed energy resources that we model based on convex operation constraints. The optimal day-ahead peak shaving control of the portfolio is realized in a distributed way through an entirely parallel implementation of the distributed ADMM optimization algorithm. In this scenario, we evaluate the ADMM algorithm's overall speed of convergence and conduct multiple large-scale numerical simulation experiments on a compute cluster to compare and assess the algorithmic performance of ADMM against a centralized reference benchmark optimization. Our simulation results show that the distributed ADMM algorithm is superior compared to a purely centralized optimization. The findings are of particular use to commercial demand response aggregators that show interest in the deployment and improvement of distributed coordination and optimal control concepts for future slow-dynamics demand response applications and services.

show abstract

Section: Admm Computational Performance Study For a Dr Peak Shaving A...mentioning

confidence: 99%

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Schwarz

Monti

2023

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…Demand flexibility [97] The electrical demand of a complex district is scheduled to provide flexibility potential for DR.…”

Section: Feedforward Neural Network and Dnnsmentioning

confidence: 99%

Appliance Level Energy Characterization of Residential Electricity Demand: Prospects, Challenges and Recommendations

et al. 2021

View full text Add to dashboard Cite

The advent of information and communication technologies has paved the way for smart cities. Residential customers are the major consumers of electrical energy in such cities. Smart meters gather the energy consumption behavior of consumers at the aggregate/household level. Characterization of aggregate demand data has several advantages but significant benefits in terms of energy sustainability require Appliance Level Energy Characterization (ALEC). Various solutions for ALEC rely on sensors, smart plugs, smart appliances, smart meters and/or energy disaggregation algorithms but smart meters with built-in energy disaggregation algorithms seem to be the most scalable option. This work is one of the pioneering contributions to present comprehensive applications and prospects of ALEC for smart residential communities. It also links these applications with 2050 decarbonization pathways and various United Nations (UN) sustainable development goals (SDGs). Prospective uses of ALEC in diverse fields such as power systems, health care, the social sciences, economics, surveillance, marketing, appliance manufacturing, technology development, etc. are highlighted. Moreover, the requirements and challenges hindering the largescale deployment of the ALEC frameworks are outlined with some recommendations and open research directions. It is envisaged that ALEC of residential electricity can be exploited not only for achieving 2050 decarbonization targets but also for several 2030 SDGs. This work will provide a one-stop source of information on ALEC and will open the doors of cooperation among various stakeholders of smart cities to achieve long-term SDGs.

show abstract

“…Reference [16] addresses the scheduling of demand in day ahead, which maximizes the usage of renewable DG. A distributed optimization algorithm is used in a case study with residential, non-residential, and industrial loads.…”

Section: Ctn Dgn Fimentioning

confidence: 99%

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Faria

Vale

2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Demand response (DR) is a flexible way to improve distributed energy resource scheduling. The innovative contribution of this paper is to include complex contracts in the model, which can accommodate the constraints according to the special expectations of each player. Such contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of consumers. Multi-period DR events are considered. In this way, consumers can specify the limits on the time, power, and remuneration regarding participation in DR events, which has not been considered in the literature. The state of the art treats these aspects separately or uses a statistical approach, without providing consumers with options to combine their preferences regarding different aspects of their flexibility deployment. The model has been validated for 218 consumers using several scenarios and different types of distributed generation, showing that it is possible to increase DR with respect to the preferences of consumers.

show abstract

Distributed Optimization for Scheduling Electrical Demand in Complex City Districts

Cited by 14 publications

References 20 publications

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Computational Performance Study on the Alternating Direction Method of Multipliers Algorithm for a Demand Response Peak Shaving Application

Appliance Level Energy Characterization of Residential Electricity Demand: Prospects, Challenges and Recommendations

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

Contact Info

Product

Resources

About