Decision Flexibility vs. Information Accuracy in Energy-intensive Businesses

Demand-side management and demand response are integral building blocks for environmental sus-tainability. Exchange-based power pricing serves as an economic mechanism to set incentives to shift demand to periods where prices are low. Low power prices also serve as an indicator for green(er) power, since high feed-ins from variable renewable sources push the electricity price downward. For businesses, minimizing electricity costs thus not only contributes to economic but also environmental sustainability. Hence, especially energy-intensive businesses can become greener and more competitive by integrating volatile electricity prices into their production planning activities. In this paper, we demonstrate that the length of the planning horizons is key to achieve more sustainable outcomes due to the trade-off between decision flexibility and information accuracy. Decision flexibility – i.e. the ca-pability to shift processes – increases with longer planning horizons. Information accuracy – i.e. price accuracy – increases with shorter planning horizons. Information Systems (IS) can help to balance this trade-off. We follow a data-driven approach and derive both actual and predicted electricity spot prices from historic electricity intraday market data in Germany. We find that decision flexibility and infor-mation accuracy affect the planning horizon as conceived. First results indicate that more sustainable outcomes are achieved with longer planning horizons

On the surplus accuracy of data-driven energy quantification methods in the residential sector

Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods

A Privacy Preserving Approach to Collaborative Systemic Risk Identification : the Use-case of Supply Chain Network

Globalization, and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network’s structure can endanger the whole supply chain network’s robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms’ competitive advantage

Chancen und Herausforderungen von DLT (Blockchain) in Mobilität und Logistik

This basic report presents the economic potential e, the legal framework and the technical fundamentals of distributed ledger or blockchain technology necessary for understanding in order to exploit the opportunities and challenges of these technologies, especially in the mobility and logistics sector. clear. The basic report was prepared on behalf of the Federal Ministry of Transport and Digital Infrastructure (BMVI) by the blockchain laboratory of Fraunhofer FIT

Opportunities and Challenges of DLT (Blockchain) in Mobility and Logistics

This report presents the economic potential, legal framework, and technical foundations required to understand distributed ledger (DL) / blockchain technology and llustrates the opportunities and challenges they present, especially in the mobility and logistics sectors. It was compiled by the blockchain laboratory at Fraunhofer FIT on behalf of the German Federal Ministry of Transport and Digital Infrastructure (BMVI). Its intended audience comprises young companies seeking, for example, a legal assessment of data protection issues related to DL and blockchain technologies, decisionmakers in the private sector wishing concrete examples to help them understand how this technology can impact existing and emerging markets and which measures might be sensible from a business perspective, public policymakers and politicians wishing to familiarize themselves with this topic in order to take a position, particularly in the mobility and logistics sectors, and members of the general public interested in the technology and its potential. The report does not specifically address those with a purely academic or scientific interest in these topics, although parts of it definitely reflect the current state of academic discussion

DECISION FLEXIBILITY VS. INFORMATION ACCURACY IN ENERGY-INTENSIVE BUSINESSES

Demand-side management and demand response are integral building blocks for environmental sustainability. Exchange-based power pricing serves as an economic mechanism to set incentives to shift demand to periods where prices are low. Low power prices also serve as an indicator for green(er) power, since high feed-ins from variable renewable sources push the electricity price downward. Thus, businesses contribute not only to economic but also environmental sustainability minimizing electricity costs. Hence, especially energy-intensive businesses can become greener and more competitive by integrating volatile electricity prices into their production planning activities. In this paper, we demonstrate that the length of the planning horizons is key to achieve more sustainable outcomes due to a trade-off between decision flexibility and information accuracy. Decision flexibility – i.e. the capability to shift processes – increases with longer planning horizons. Information accuracy – i.e. price accuracy – increases with shorter planning horizons. Information Systems (IS) can help to balance this trade-off. We follow a data-driven approach and derive both actual and predicted electricity spot prices from historic electricity intraday market data in Germany. We find that decision flexibility and information accuracy affect the planning horizon as conceived. First results indicate that more sustainable outcomes are achieved with longer planning horizons

Shifting load through space - the economics of spatial demand side management using distributed data centers

Demand-side flexibility (DSF) in the electricity grid has become an active research area in recent years. While temporal flexibility (e.g. load shedding, load shifting) is already discussed intensively in literature, spatial load migration still is an under-researched type of DSF. Spatial load migration allows us to instantly migrate power-consuming activities among different locations. Data centers (DCs) are power-intensive and process information goods. Since information goods are easily transferable through communication networks, power-intensive processing of information goods is not necessarily tied to a specific location. Consequently, geographically distributed DCs inherit - in theory - a considerable potential to globally migrate load. We analyze the economics of spatially migrating load to provide balancing power using geographically distributed DCs. We assure that neither of the participating electricity grids will be burdened by this mechanism. By using historical data to evaluate our model, we find reasonable economic incentives to migrate positive as well as negative balancing power. In addition, we find that current scenarios favor the migration of negative balancing power. Our research thus reveals realistic opportunities to virtually transfer balancing power between different market areas worldwide