Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

Fleschutz, Markus; Bohlayer, Markus; Braun, Marco; Murphy, Michael D.

doi:10.3390/su14138025

Cited by 13 publications

(6 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For other applications with grey-box models, multiple solutions exist. Fleschutz et al [44] propose an energy optimization software framework for industrial applications on a system level, including multiple energy sources, energy storage systems and data-driven load analysis. Within the framework, multiple DR aspects can be optimized, including the peak load.…”

Section: Process Load Modeling and Estimationmentioning

confidence: 99%

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

Leherbauer,

Hehenberger

2024

Sustainability

View full text Add to dashboard Cite

The transition to sustainable energy sources presents significant challenges for energy distribution and consumption systems. Specifically, the intermittent availability of renewable energy sources and the decreasing usage of fossil fuels pose challenges to energy flexibility and efficiency. An approach to tackle these challenges is demand-side management, aiming to adapt energy consumption and demand. A key requirement for demand-side management is the traceability of the energy flow among individual energy consumers. In recent years, advancements in industrial information and communication technology have provided additional potential for data acquisition. Complementary to acquired data, a physics-based modeling and analysis approach is proposed, which describes the energy consumption with physical parameters. This results in comprehensive options for monitoring actual energy consumption and planning future energy demand supporting energy efficiency and demand-response goals. To validate the proposed approach, a case study with a 3D printer covering approximately 110 h of active printing time is conducted. The medium-term study results indicate a consistent parameter trend over time, suggesting its conceptual suitability for industrial application. The approach helps to monitor energy efficiency among manufacturing assets by identifying peak loads and consumption hotspots, and provides parameters to estimate energy consumption of manufacturing processes. Results indicate up to 50% energy savings when switching the printing material and indicate further potentials.

show abstract

Section: Process Load Modeling and Estimationmentioning

confidence: 99%

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

Leherbauer,

Hehenberger

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…The high share of integrated RES contributes more economical benefits, reduces greenhouse emissions from the environment, and provides better energy security. To analyze the economic and environmental benefits, a DR program was conducted on the residential MG system [27][28][29][30][31], CHP-based reconfigurable MG system [32], demand response analysis framework with multiple BSS [33], and CHP-based MG with multiple markets [34]. The cost of the MG emitted emission and the demand cost are optimized simultaneously by linear programming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A new demand response analytical framework (DRAF) was analyzed in a python environment for (i) DR in the production process, (ii) design optimization of battery with a PV system, and (iii) DR of distributed thermal energy resources. It was observed that using Pareto front through Price based DR, cost and emissions are reduced [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

Kujur¹,

Dubey²,

Salkuti

2023

Sustainability

View full text Add to dashboard Cite

In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach.

show abstract

“…To quantify the value of flexibility for this company, we formulate a deterministic MILP using technology components of version v0.3.0 [40] of the open-source Python Demand Response Analysis Framework (DRAF), a modular tool for economic and environmental evaluation of DR [41]. An overview of the model is shown in Fig.…”

Section: Overviewmentioning

confidence: 99%

“…DRAF and most used technology components are detailed in [41]. However, model parts that are crucial (cost and carbon balances, HP, BEV) or not described in [41] such as wind turbine (WT), pressurized H 2 storage (H 2 S), proton exchange membrane fuel cell (FC), proton exchange membrane electrolyzer (Elc), and direct air capture (DAC) are described in this section. We model one year with 8,760 hourly time steps t ∈ T , so ∆t = 1 h. We assume perfect foresight.…”

Section: Overviewmentioning

confidence: 99%

From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company

Fleschutz¹,

Bohlayer²,

Braun³

et al. 2023

Preprint

View full text Add to dashboard Cite

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while stabilizing the electricity system. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company's MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of powered industrial trucks, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal energy, electricity, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19-80% depending on electricity and carbon removal prices), total annual costs, operating carbon emissions, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs. Input Results MILP model of industrial MESCarbon offset prices elec. prices of two years hourly CEFs hourly elec. prices Wind turbine (kW/kW p ) PV (kW/kW p ) BEV availability Carbon emission constraints

show abstract

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

Cited by 13 publications

References 62 publications

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company

Contact Info

Product

Resources

About