Effects of Constraints in Residential Demand-Side-Management Algorithms—A Simulation-Based Study

Behrens, Dennis; Rüther, Cornelius; Schoormann, Thorsten; Hachmeister, Thimo; Ambrosi, Klaus; Knackstedt, Ralf

doi:10.1007/978-3-319-89920-6_35

Cited by 2 publications

(2 citation statements)

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“…Some DR models can be traced back to this generalized model [13]. Because we want to derive a single optimization function model and we have to consider several constraints regarding the appliances [30] to meet the application area, we choose the optimization model of [31], based on [30], which aims at reducing the costs of the overall system with the help of a ToUP, which can be exchanged to several other DP alternatives. Our model uses the following variables:…”

Section: B Model From the Dr Fieldmentioning

confidence: 99%

“…As these are additional objectives, they need to be included in the optimization function (done in [13] in a generalized form for a single home). Nevertheless, authors use multiple optimization functions, which result in an even more complex problem and the algorithm gets inefficient, for example, regarding the calculation time [31]. We therefore need less objective functions [33].…”

Section: A Adapted Optimization Modelmentioning

confidence: 99%

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Developing an Algorithm to Consider Mutliple Demand Response Objectives

Behrens

Schoormann

Knackstedt

2018

Eng. Technol. Appl. Sci. Res.

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Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.

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