Demand side management for city districts

Müller, Dirk; Monti, Antonello; Stinner, Sebastian; Schlösser, Tim; Schütz, Thomas; Matthes, Peter; Wolisz, Henryk; Molitor, Christoph; Harb, Hassan; Streblow, Rita

doi:10.1016/j.buildenv.2015.03.026

Cited by 65 publications

(28 citation statements)

References 29 publications

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“…Some of these optimization methods are namely, mixed-integer linear programming (MILP) and dynamic programming for a global optimal solution. Further on, metaheuristic methods such as particle swarm optimization and evolutionary algorithms have been implemented by many researchers [22]. It could be understood that for given electricity consumption requirements, an optimization problem can be derived based on the power contracting plan, i.e.…”

Section: Optimizationmentioning

confidence: 99%

“…Demand side management (DSM) is a proactive way to increase the energy efficiency among customers in the long-term [19], and can reduce both the electricity peak power demand (kW) and the electricity consumption (kWh) [20,21]. The most prominent DSM methods include reducing peak loads (peak clipping or peak shaving), shifting load from on-peak to off-peak (load-shifting), increasing the flexibility of the load (flexible load shape), and reducing energy consumption in general (strategic conservation), as stated by Müller et al [22].…”

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confidence: 99%

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Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

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Cooling in the industry sector contributes significantly to the peak demand placed on an electrical utility grid. New electricity tariff structures include high charges for electricity consumption in peak hours which leads to elevated annual electricity costs for high-demanding consumers. Demand side management (DSM) is a promising solution to increase the energy efficiency among customers by reducing their electricity peak demand and consumption. In recent years, researchers have shown an increased interest in utilizing DSM techniques with thermal energy storage (TES) and solar PV technologies for peak demand reduction in industrial and commercial sectors. The main objective of the present study is to address the potential for applying optimization-based time-of-use DSM in the industry sector by using cold thermal energy storage and off-grid solar PV to decrease and shift peak electricity demands and to reduce the annual electricity consumption costs. The results show that when cold thermal energy storage and solar PV are coupled together higher annual electricity cost savings can be achieved compared to using these two technologies independently. Additionally, considerable reductions can be seen in electricity power demands in different tariff periods by coupling thermal energy storage with off-grid solar PV.

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Section: Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

et al. 2018

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“…The slow thermal dynamics and imprecise nature of the thermal objectives in buildings (occupants typically require ambient temperatures to be within a band, rather than matching a specific single set-point) introduces the opportunity for wider system optimisation. Without diminishing comfort in the building, loads can be temporally shifted [48,49], allowing (with appropriate control) for the satisfaction of supply constraints and an improvement in the performance of the overall energy system. A multi-objective optimisation approach to do just this is introduced in [50] for example.…”

Section: Demand-side Management In Smart Citiesmentioning

confidence: 99%

“…On the electrical side, load shifting through appropriate control of heatpumps and thermal storage is discussed in [49]. Such load shifting approaches can reduce required installed capacity as well as reducing stress on the wider system though monetising, incentivising and fairly distributing the financial gains from this wider system operational benefit is an open challenge.…”

Section: Advanced Control Strategiesmentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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“…aaSome DSM concepts for guaranteeing the correct electrical demand and supply balancing—in the presence of renewable energy—are presented by Müller et al …”

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confidence: 99%

Energy forecasting tools and services

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Waczowicz

Hagenmeyer

et al. 2017

WIREs Data Min & Knowl

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The increasing complexity of the power grid and the continuous integration of volatile renewable energy systems on all aspects of it have made more precise forecasts of both energy supply and demand necessary for the future Smart Grid. Yet, the ever increasing volume of tools and services makes it difficult for users (e.g., energy utility companies) and researchers to obtain even a general sense of what each tool or service offers. The present contribution provides an overview and categorization of several energy‐related forecasting tools and services (specifically for load and volatile renewable power), as well as general information regarding principles of time series, load, and volatile renewable power forecasting. WIREs Data Mining Knowl Discov 2018, 8:e1235. doi: 10.1002/widm.1235 This article is categorized under: Application Areas > Business and Industry Application Areas > Data Mining Software Tools Technologies > Prediction

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Demand side management for city districts

Cited by 65 publications

References 29 publications

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Energy forecasting tools and services

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