• Energy Research

In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mechanisms include Peer-to-Peer energy trading, Community Self-Consumption and Transactive Energy Models. To ensure the stable and reliable delivery of electricity as such markets and models grow, this paper aims to understand the impact of these models on grid infrastructure, including impacts on the control, operation, and planning of power systems, interaction between multiple market models and impact on transmission network. Here, we present a comprehensive review of existing research on impact of Local Energy Market integration in power systems layer. We detect and classify most common issues and benefits that the power grid can expect from integrating these models. We also present a detailed overview of methods that are used to integrate physical network constraints into the market mechanisms, their advantages, drawbacks, and scaling potential. In addition, we present different methods to calculate and allocate network tariffs and power losses. We find that financial energy transactions do not directly reflect the physical energy flows imposed by the constraints of the installed electrical infrastructure. In the end, we identify a number of different challenges and detect research gaps that need to be addressed in order to integrate Local Energy Market models into existing infrastructure. + ID der Publikation: hslu_86628 + Art des Beitrages: Wissenschaftliche Medien + Jahrgang: 301 + Sprache: Englisch + Letzte Aktualisierung: 2023-04-17 10:46:41

This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the ‘Present-day’ and the ‘High’ scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms.

In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device's energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards. Published in Sustainable Energy, Grids and Networks (SEGAN) 2022

The power system requires an additional amount of flexibility to process the large-scale integration of renewable energy sources. Community Energy Storage (CES) is one of the solutions to offer flexibility. In this paper two scenarios of CES ownership are proposed. Firstly, an Energy Arbitrage (EA) scenario is studied where an aggregator aims to minimize costs and CO2-emissions of an energy portfolio. Secondly, an Energy Arbitrage - Peak Shaving (EA-PS) scenario is assessed, which is based on a shared ownership between a Distribution System Operator (DSO) and an aggregator. A multi-objective Mixed Integer Linear Programming (MILP) optimization model is developed to minimize the operation costs and CO2-emissions of a community situated in Cernier (Switzerland), using different battery technologies in the CES system. The results demonstrate a profitable system design for all Lithium-ion-Batteries (LiBs) and the Vanadium Redox Flow Battery (VRFB), for both the EA and EA-PS scenarios. The economic and environmental performance of the EA-PS scenario is slightly worse compared to the EA scenario, due to power boundaries on grid absorption and injection to achieve peak shaving. Overall, the differences between the EA and EA-PS scenarios, in economic and environmental performance, are small. Therefore, the EA-PS is recommended to prevent problematic loads on the distribution transformer. In addition, the Pareto frontiers demonstrate that LiBs perform best on both economic and environmental performance, with the best economic and environmental performance for the Lithium-Nickel-Manganese-Cobalt (NMC-C) battery.

Accurate forecasts of the electric power generation by solar Photovoltaic (PV) systems are essential to support their vast increasing integration. This study evaluates the interdependence of 14 predictor variables and their importance to machine learning models that forecast the day-ahead PV power production. To this purpose, we use two feature selection models to rank the predictor variables and accordingly, examine the performance change of two forecast models when a growing number of variables is considered. The study is performed using 3 years of data for Utrecht, the Netherlands. The results show the most important variables for PV power forecasting and identifies how many top variables should be considered to achieve an optimal forecast performance accuracy. Additionally, the best forecast model performance is found when only a few predictor variables are considered, including a created variable that estimates the PV power output based on technical system characteristics and physical relations.