Active consumer participation in smart energy systems

Schweiger, Gerald; Eckerstorfer, Lisa V.; Hafner, Irene; Fleischhacker, Andreas; Radl, Johannes; Glock, B.; Wastian, Matthias; Rößler, M.; Lettner, Georg; Popper, Niki; Corcoran, Katja

doi:10.1016/j.enbuild.2020.110359

Cited by 53 publications

(20 citation statements)

References 105 publications

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“…The time leap constraint was applied in every period that represents a day transition, time periods 192, 384, 576, 768, and 960, which corresponds to the final time period in each day (23:00 of Monday, Tuesday, Wednesday, Thursday, and Friday). A task order constraint where every task "Harden [1.5]" must be preceded by a task "Harden [2]", a task collision constraint between tasks "Sublimation" and "Harden [2]", and a product request deadline, in a product, of time period 960, which corresponds to a deadline of 23:00 of Friday. During the week, 132 units of 14 products were requests.…”

Section: Resultsmentioning

confidence: 99%

“…The same problem was addressed by Shen et al (2018) using a q-learning-based memetic algorithm [18] and by Xiao et al (2019) using a multi-objective ant colony optimization [19]. 2…”

Section: Introductionmentioning

confidence: 86%

“…Power and energy systems are going through a paradigm change where distributed management, distributed generation, and end-user participation are promoted and needed [1]. The end-user participation is key for the proliferation of smart grids and needs to be addressed [2]. The use of end-users flexibility in demand-side management methodologies can increase the use of local renewable energy generation and enable their participation in demand response programs [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

et al. 2021

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The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

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Section: Resultsmentioning

confidence: 99%

“…The same problem was addressed by Shen et al (2018) using a q-learning-based memetic algorithm [18] and by Xiao et al (2019) using a multi-objective ant colony optimization [19]. 2…”

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

et al. 2021

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“…Then, similar actions are performed for the electric supply. An application is formed (6), then it is sent to the network agent (7). A decision on electrical supply comes from the network agent and the consumer agent checks it for compliance with the sent request (8), followed by a transition to one of the possible states: energy received (9) or energy not received (10).…”

Section: Multiagent Modelmentioning

confidence: 99%

“…Consumers who are proactive in seeking a great deal and advocating for their interests can benefit from lower energy prices or the opportunity to improve service quality. The paper [7] provides an overview of the required steps and difficulties in developing and implementing user-centered business models, including consideration of the required data, computational methods and psychological aspects of consumer participation. In the study [8], the authors develop a realtime pricing scheme that provides the necessary level of financial incentives for consumers, equitably rewarding desired changes in electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Application of a multiagent approach for optimal load distribution between sources in an integrated energy system taking into account the operation of active consumers

2021

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In the context of the energy transition, research on the creation of integrated energy systems and their control is an actual task. They combine a significant share of renewable energy sources, contribute to the overall efficiency of the system, and enable active consumers to participate in the energy supply process. At the same time, in connection with the growing capacity of distributed energy sources, new problems arise related to the operation of distribution networks and difficulties in adapting the operation of active consumers in the centralized energy system. Active consumers can regulate their energy consumption by distributing the load between centralized and distributed energy sources, and as a result, they can provide flexibility, maneuverability in the operation of the system and increase the efficiency of its redundancy. To organize the work of active consumers in an integrated energy system, a multiagent approach is used. This approach is widely used by researchers to solve various practical tasks. Allows us to represent active consumers in the form of agents with an individual behavior algorithm and organize their interaction with the energy system to ensure optimal energy supply. The proposed mathematical model for finding the optimal composition of generating capacity takes into account the structural organization of centralized and distributed energy sources, as well as the participation of active consumers in the process of energy supply. Modeling of an integrated energy system and carrying out a computational experiment based on multiagent technologies are performed in the AnyLogic software environment.

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Volt/var Chain Process*

Schultis

Ilo

2021

A Holistic Solution for Smart Grids Based on LINK– Paradigm

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Simplicity is the ultimate sophistication.-Leonardo da Vinci IntroductionPhysically, the voltage is a local quantity that differs in each node of the electricity grid, as it varies along with the power transfer and the characteristics of the grid. Due to their strong coupling, the reactive power has always been used to control the voltage in transmission grids, originating the term Volt/varprocess. Reactive power is produced and consumed by diverse appliances distributed throughout the power system and operated by different stakeholders, making Volt/var control an extremely complex issue by its very nature. The overall control strategy should be designed with special care to reduce the complexity of the resulting Volt/var chain to facilitate the management.Volt/var management is one of the most fundamental power system processes that enables the reliable and efficient operation of the electricity grids. It is used to ensure compliance to voltage limits and to keep the power factor close to one. Both the elimination of voltage limit violations and the minimisation of the reactive power flows extend the active power transfer capability of the grid. In other words, an appropriate Volt/var control increases the utilisation of the electrical infrastructure, thus lowering the need for grid reinforcement.The massive connection of renewable and distributed generation and the electrification of other sectors gave rise to new challenges and opportunities that call for an adaption of the traditional Volt/var control schemes. Deteriorated power quality and uncontrolled interactions between the systems operated by different stakeholders are recognised as the main problems to be solved by future smart grids [66]. Meanwhile, the ability of distributed energy resources to participate in the Volt/var process

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Active consumer participation in smart energy systems

Cited by 53 publications

References 105 publications

Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

Application of a multiagent approach for optimal load distribution between sources in an integrated energy system taking into account the operation of active consumers

Volt/var Chain Process*

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