Transactive Control of Coupled Electric Power and District Heating Networks

The aim to decarbonize the energy supply represents a major technical and social challenge. The design of approaches for future energy network operation faces the technical challenge of needing to coordinate a vast number of new network participants spatially and temporally, in order to balance energy supply and demand, while achieving secure network operation. At the same time these approaches should ideally provide economic optimal solutions. In order to meet this challenge, the research field of transactive control emerged, which is based on an appropriate interaction of market and control mechanisms. These approaches have been extensively studied for electric power networks. In order to account for the strong differences between the operation of electric power networks and other energy networks, new approaches need to be developed. Therefore, within this work a new transactive control approach for Coupled Electric Power and District Heating Networks (CEPDHNs) is presented. As this is built upon a model-based control approach, a suitable model is designed first, which enables to operate coupled electric power and district heating networks as efficient as possible. Also, for the transactive control approach a new fitted procedure is developed to determine market clearing prices in the multi-energy system. Further, a distributed form of district heating network operation is designed in this context. The effectiveness of the presented approach is analyzed in multiple simulations, based on real world networks

Coordination of smart home energy management systems in neighborhood areas: A systematic review

High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers' consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills

Centralised and distributed optimization for aggregated flexibility services provision

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.Peer ReviewedPostprint (published version

Advances in Energy System Optimization

The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme “Bridging the Gap Between Mathematical Modelling and Policy Support” on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart

Cloud-based energy management service: Design, analysis, and realization

With the aroused attentions on promoting renewable energy and the increasing penetration of distributed energy resources (DER) and the electric vehicles (EVs), providing the energy management tools efficiently for operating DERs and EVs grid-friendly and attracting customers to involve the management have become the important issues. An extensive cloud-based framework is firstly proposed to provide the energy management as a service (EMaaS) for customers (i.e., DERs owners). Customers who are involved in the same EMaaS form the ``community to trade their produced renewable energy virtually among others. By facilitating the DERs, storage systems, and the customers\u27 trading choices within the same community, incentives are maximized as the global cost is minimized and renewable energy integration is enhanced as the renewable energy consumption is stabilized by the proposed EMaaS for each community. To further attract customers not only involve in controlling their consumption patterns but also participate actively, and operate EVs and DERs within the community grid-friendly, the fair demand response with the EV is secondly realized for the cloud-based energy management service (F-DREV). The choices of electricity usage and trading are combined to further minimize the global cost for each community while distributing incentives fairly to the individual customer. The cross-community interaction (XCI) and adjustment (XCI) are thirdly proposed for the cloud-based energy management. XCI minimizes the global costs for the collaborated communities and is performed in the distributed fashion to overcome the privacy concern and the difficulty for handling the large-scale data. XCA enhances the efficiency of XCI under uncertainty, where the overwhelmed data exchanging and the computations can be significantly reduced