A Decentralized Framework for the Optimal Coordination of Distributed Energy Resources

Anjos, Miguel F.; Lodi, Andrea; Tanneau, Mathieu

doi:10.1109/tpwrs.2018.2867476

Cited by 45 publications

(21 citation statements)

References 34 publications

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“…From the obtained results, the proposed model improved demand profile by 33% and 40% for hot and cold weather conditions, respectively, compared to the conventional energy management system. Similar research can be found in [20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Methodssupporting

confidence: 82%

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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Section: Methodssupporting

confidence: 82%

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

Essiet

Sun

2020

Applied Sciences

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“…Some of (centralized) deterministic algorithms, e.g., [18], rely on (commercial) solvers which may not be scalable to very large systems to meet the requirement of realtime control. b) Controlling Devices with Dynamics: Controllable devices with dynamics are usually handled in two ways: control based on heuristic or engineering intuition, see, e.g., [19] that controls TCLs based on temperature status; and optimizationbased control that can integrate specific objective functions and constraints, see, e.g., [20] that considers device dynamics but does not involve discrete variables, [21] that considers device dynamics and discrete decision variables but employs commercial solvers, [22], [23] that solve OPF over various devices with dynamics but consider equality constraints only, and [24], [25] that formulate mixed-integer linear programs which cannot be applied to solving more general problems with convex cost functions. c) Market-Based/Demand-Response Design: Existing literature on market-based and demand-response problem formulation mostly focuses on demand/supply balancing, without considering network structure, see, e.g., [3]- [6], [20], [21], [26].…”

Section: A Related Workmentioning

confidence: 99%

Online Stochastic Optimization of Networked Distributed Energy Resources

Zhou

Dall’Anese

Chen

2020

IEEE Trans. Automat. Contr.

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This paper investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multi-period social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. We further extend it to an online realtime setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.

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“…The research initiative Optimization for Smart Grids (OSG) was founded by a consortium of research institutes and the Polytechnique Montreal and is focused on the application of mathematical optimization techniques to the planning and operation of modern power systems. In particular, the areas in which the initiative is conducting research are system load management and DR, modeling, management and operation of smart buildings, maintenance scheduling in hydropower systems, location and pricing of EV charging stations, and the optimal provision of reactive power in a large‐scale grid …”

Section: Smart Grid Labs Descriptionmentioning

confidence: 99%

Smart grid lab research in Europe and beyond

et al. 2019

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Summary In this survey, the objective is to identify current trends in the smart grid research by exploring the work carried out in numerous smart grid labs worldwide. For this purpose, a large number of smart grid labs are identified, and a short description of their activities is given. Fifty‐eight out of the 75 identified labs are located in Europe. Smart grid research is divided into categories, which represent popular topics of research in the field. The predominant category of research is identified to be generation and distributed energy resources (Gen & DER) with 91% of the labs conducting research in this field. Aggregated information is presented regarding the labs, providing a clear idea of the topics of research carried out. Connections between different topics of research are presented, which reveal synergies or collaboration gaps among various smart grid topics. Grid management and Gen & DER and energy storage and Gen & DER have been found to be popular combinations of topics with 55 labs active in both, respectively. In addition, we provide insights on the entities at which research is targeted and consider the evolution of publications produced by the labs on the different categories. An overall increase in publications was observed over the past 11 years in virtually all categories of smart grid research with the most published scientific papers in Gen & DER and electromobility. Collaborations between research institutes have been analyzed, pointing out existing joint research conducted and the huge potential to explore synergies between institutes further. Our work is useful in order to identify the smart grid areas where research is focusing on. This gives a clear picture of potential synergies between labs for knowledge sharing and enhancing their research efforts.

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A Decentralized Framework for the Optimal Coordination of Distributed Energy Resources

Cited by 45 publications

References 34 publications

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

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

Online Stochastic Optimization of Networked Distributed Energy Resources

Smart grid lab research in Europe and beyond

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