• Energy Research

Reliability of power system in terms of investments in network maintenance and restructuring for power distribution network has gained importance due to increase in distributed generation. To determine the reliability of the power distribution network, the state of power apparatus, losses in the network and consumer satisfaction indices are key factors. Considering the aforementioned, this paper proposes a holistic reliability framework for power distribution networks. The framework lists the following factors: life cycle of power apparatus, environmental and sociological, node reliability, arc reliability. A case study for reliability evaluation is performed on a modified IEEE 14 bus network. Furthermore, multiple scenarios of generation fault or outage are studied and results are presented. The key contribution of this paper is to present a novel and holistic reliability framework to model distribution network.

Abstract Power system planning for modern distribution networks is undergoing a change because of distributed power generation and grid ancillary services. The vast electricity retail utility industry with many distribution network operators plans generation and transmission expansion planning for determining optimal investment decisions. This paper addresses this planning problem in a decentralized and distributed context. This paper introduces a coordinated decision making approach for optimal investments in generation and transmission expansion planning problems for distribution networks. The distribution networks are further classified into microgrids. Considering an agent as the functional information exchanging entity just like an energy meter with the objective to coordinate the expansion decisions of participants; a novel math-heuristic optimization model Coordinated Microgrid is presented. To simulate the coordination of information a multi-agent-system based coordinated decision making method is adopted and the value of coordination is investigated. The evolutionary vertical sequencing protocol, a heuristic method, is developed and implemented to simulate the coordination process among agents on the top level. The proposed protocol produces smart permutations of microgrids for coordination. On the bottom level, a two-stage chance-constrained stochastic MILP formulation for investment decisions with operational uncertainties is modeled. For market clearing a nodal-pricing scheme is adopted that maintains the Nash equilibrium among and across the microgrids for energy transactions. The proposed model is tested with consumption, network configuration data from three islands in west-coast of Norway. The models are solved to optimality and results lead to the observations that the value of coordination lies in profit increment of individual microgrid. The novel protocol proposed demonstrates an advantage of retrieving smart permutations from combinations of microgrids. In summary, CoMG is a novel expansion planning model for optimal investments in modern power distribution networks.

The increase of production from wind power plants and photovoltaics with changing market conditions are changing the energy system as we have known. Estonia has set a target to increase the consumption from renewable energy by 2030 to 50%, which would mean that there is a need for about additional 2.3 TWh of electricity generated from renewables by 2030. The maximum flexibility of the existing energy system in integration of renewable energy is not yet analyzed. The EnergyPLAN software is used in the study to determine what is the technical-economical limitation of additional wind and photovoltaic that can be added to the system. The results demonstrate despite the additional renewable energy generation the conventional oil shale based power plants which are mostly influence by the CO2 price. Even when the Estonian 2030 renewable energy targets would multiple the renewable energy production, because of the high CO2 price Estonia would transform to electricity importing country.

Abstract Harvesting energy from renewable sources has become prominent since the use of fossil fuels became unsustainable. Traditional practice for mitigating the energy demand around globe majorly consists of utilizing conventional sources and injection of renewables as and when available. The continuous and exponential growth in consumption alongside the need to reduce the carbon footprint and to counter the climate change has paved the way for Renewable Energy Sources (RES). Availability and maturity in technology made wind and PV (photo-voltaic) the most prominent among others. Per contra, the inherent variations in the weather in form of wind speed, solar irradiance act as a barrier in utilizing the full potential. The variations, ramp events, in case of wind energy have adverse effects on determining the reliability, economical profitability, and flexibility. Accurate recognition of the wind ramp events can improve energy management, forecasting and causality. This paper proposes a data analysis oriented approach exploring the pre-processing technique of wind power variations using moving average filter, followed by noise extraction and separating the power swings. Further clustering the power swings utilizing K-means clustering technique. The proposed technique improves the power swings identification process by reducing the noise content.

This paper presents a techno-economic optimisation tool to study how the power system expansion decisions can be taken in a more economical and efficient way, by minimising the consequent costs of network reinforcement and reconfiguration. Analyses are performed to investigate how the network reinforcement and reconfiguration should be planned, within a time horizon of several years, by continuously keeping the network feasibility and ability to satisfy the load. The main contribution of this study is the inclusion of key features within the mathematical model to enhance the investment decision making process. A representative maintenance cost of existing cables and apparatus is included, to analyse the influence of the historical performance of the electric items on the investment decisions. A multihorizon methodology is developed to take into account the long term variation of the demand, combined with the long term variation of cables maintenance costs. Moreover, technological learning coefficients are considered, to take into account the investment costs reductions that arise when an investment in network restructuring and/or reconfiguration is repeated throughout the years. Finally, construction time constraints are included to find a proper investment scheduling that allows a feasible power flow, also during the years required to build a new connection or restructure an existing one. This study is also providing recommendations for future research directions within the power system reliability field. The analyses show the important and urgent need of proper methodologies for a better definition of cables projected maintenance costs and learning coefficients dedicated to network restructuring, reconfiguration and expansion. 22 pages, 21 figures

Abstract This paper aims at identifying the key role of ”Smart Energy and Power Systems Modelling”, within the context of Energy Informatics. The main objective is to describe how the specific subject of ”Smart Energy and Power Systems Modelling” can give a key contribution within the novel domain of Energy Informatics, by successfully linking and integrating the different disciplines involved. First the paper will present how and where the specific subject of ”Smart Energy and Power Systems Modelling” can position itself within the broad Energy Informatics domain. Afterwards the paper will explain how Cyber-Physical Systems (CPS) and Information and Communication Technologies (IoT), coupled with ”Smart Energy and Power Systems Modelling”, can enhance the Energy Informatics domain. In addition, the main challenges and opportunities for Energy Informatics specialists will be outlined, with regard to the interdisciplinary approach that characterises this field.

Abstract 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. Article highlights Didactic approaches in the energy informatics domain are discussed based on research and industrial trends. Research trends and industrial innovation driven by energy informatics are investigated. A fundamental framework for an energy informatics course is defined together with specialized distinguishing features.