Ancillary service provision by demand side management : a real-time power hardware-in-the-loop co-simulation demonstration

The role of demand side management in providing ancillary services to the network is an active topic of research. However, their implementation is limited due to lack of practical demonstrations and tests that can rigorously quantify their ability to support the grid’s integrity. In this paper, provision of time critical frequency control ancillary service is demonstrated by means of integrating PowerMatcher, a well discussed demand side management mechanism in literature, with real-time power hardware. The co-simulation platform enables testing of demand side management techniques to provide ancillary services

Optimal Control of CHP Plant Integrated with Load Management on HVAC System in Microgrid

© 2019 IEEE. Combined heat and power (CHP) is a typical community owned distributed generation solution in microgrid development. In this work, the ratio between the electricity output and the thermal output is controlled, along with the demand side load management, so as to minimize the overall microgrid operational cost. A model is established for the energy cost of a smart building system, which includes factors such as the real time electricity pricing, the capacity and constraints within CHP operation, the operating condition of heating, ventilation, and air - conditioning (HVAC), and the indoors air temperature of the smart building. Efficient CHP operation and HVAC load management under demand response (DR) are determined through optimization. A case study is carried out to examine the effectiveness of the proposed strategy

Multi-agent system implementation in demand response: A literature review and bibliometric evaluation

This research provides a comprehensive literature overview and bibliometric evaluation of multi-agent system (MAS) implementation in energy demand response (DR) to identify gaps. The review encompasses 39 relevant papers from searches in three academic databases, focusing on studies published from 2012 to the middle of 2023. The review includes MAS frameworks, optimization algorithms, communication protocols, market structures and evaluation methodologies. Bibliometric analysis of 587 documents from the search on the Scopus database identified prolific authors, influential articles and collaborative networks within the field. The findings reveal growing research interest in implementing an MAS for DR, focusing on integrating intelligent agents into electricity grids to enable effective load management and enhance grid stability. Additionally, the review outlines potential research directions, including exploring advanced MAS techniques, interoperability challenges, policy implications and the integration of renewable energy sources

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges

Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

Traditional power systems were typically based on bulk energy services by large utility companies. However, microgrids and distributed generations have changed the structure of modern power systems as well as electricity markets. Therefore, restructured electricity markets are needed to address energy transactions in modern power systems. In this dissertation, we developed a hierarchical and decentralized electricity market framework for multi-microgrid systems, which clears energy transactions through three market levels; Day-Ahead-Market (DAM), Hour-Ahead-Market (HAM) and Real-Time-Market (RTM). In this market, energy trades are possible between all participants within the microgrids as well as inter-microgrids transactions. In this approach, we developed a game-theoretic-based double auction mechanism for energy transactions in the DAM, while HAM and RTM are cleared by an optimization algorithm and reverse action mechanism, respectively. For data exchange among market players, we developed a secure data-centric communication approach using the Data Distribution Service. Results demonstrated that this electricity market could significantly reduce the energy price and dependency of the multi-microgrid area on the external grid. Furthermore, we developed and verified a hierarchical blockchain-based energy transaction framework for a multi-microgrid system. This framework has a unique structure, which makes it possible to check the feasibility of energy transactions from the power system point of view by evaluating transmission system constraints. The blockchain ledger summarization, microgrid equivalent model development, and market players’ security and privacy enhancement are new approaches to this framework. The research in this dissertation also addresses some ancillary services in power markets such as an optimal power routing in unbalanced microgrids, where we developed a multi-objective optimization model and verified its ability to minimize the power imbalance factor, active power losses and voltage deviation in an unbalanced microgrid. Moreover, we developed an adaptive real-time congestion management algorithm to mitigate congestions in transmission systems using dynamic thermal ratings of transmission lines. Results indicated that the developed algorithm is cost-effective, fast, and reliable for real-time congestion management cases. Finally, we completed research about the communication framework and security algorithm for IEC 61850 Routable GOOSE messages and developed an advanced protection scheme as its application in modern power systems

Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends

Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power systems in a holistic manner. The objective of this paper is to draw an overview of the novel domain of energy informatics by addressing the educational opportunities as well as related challenges in light of current trends and the future direction of research and industrial innovation. In this study we discuss the energy informatics domain in a way that goes beyond a purely scientific research perspective. This paper widens the analyses by including reflections on current and future didactic approaches with industrial innovation and research as a background. This paper provides key recommendations for the content of a foundational introductory energy informatics course, as well as suggestions on distinguishing features to be addressed through more specialized courses in the field. The importance of this work is based on the need for better guidelines for a more appropriate education of a new generation of experts who can take on the novel interdisciplinary challenges present in future integrated, sustainable energy systems

Community Renewable Energy Networks in urban contexts: the need for a holistic approach

Despite a ubiquitous interest in community energy, a review of the literature reveals a fragmented approach in which the technology elements that need to be considered for the effective existence of CREN are well understood but the social aspects have not yet been addressed to the same degree.   Thus, while technology is no longer the limiting factor it used to be and there are mechanisms that can be used to deal with the social requirements, the fragmentation remains a challenge.  The next necessary step in the exploration of community renewable energy lies in crafting a holistic approach that brings it all together to foster successful implementations.  The aim of this paper is to define an urban CREN within this holistic outlook and review the literature that refers to the different aspects that need to be considered for project success in a greenfield setting.  In conclusion, the authors suggest the reconceptualisation of CREN as an organisation to create a business model in which the technology and social aspects are approached in a transdisciplinary manner to achieve the effective creation and ongoing operation of such networks. 

Mathematical framework for designing energy matching and trading within green building neighbourhood system

Nowadays, energy efficiency, energy matching and trading, power production based on renewable energyresources, improving reliability, increasing power quality and other concepts are providing the most important topics in the power systems analysis especially in green building in the neighbourhood systems (GBNS). To do so, the need to obtain the optimal and economical dispatch of energy matching and trading should be expressed at the same time. Although, there are some solutions in literature but there is still a lack of mathematical framework for energy matching and trading in GBNS. In this dissertation, a mathematical framework is developed with the aim of supporting an optimal energy matching and trading within a GBNS.This aim will be achieved through several optimization algorithms based on heuristic and realistic optimization techniques. The appearance of new methods based on optimization algorithms and the challenges of managing a system contain different type of energy resources was also replicating the challenges encountered in this thesis. As a result, these methods are needed to be applied in such a way to achieve maximum efficiency,enhance the economic dispatch as well as to provide the best performance in GBNS. In order to validate theproposed framework, several case studies are simulated in this thesis and optimized based on various optimization algorithms. The better performances of the proposed algorithms are shown in comparison with the realistic optimization algorithms, and its effectiveness is validated over several GBs. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition. The obtained results clearly show that the proposed framework is effective in achieving optimal dispatch of generation resources in systems with multiple GBs and minimizing the market clearing price for the consumers and providing the better utilization of renewable energy sources