Special issue on standalone renewable energy system: Modeling and controlling

1. Introduction Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid. These systems may include photovoltaic generators, wind turbines, hydro turbines or any other renewable electrical generator. Usually this kind of system includes electricity storage (commonly, lead-acid batteries, but also other types of storage can be used, such as lithium batteries, other battery technologies, supercapacitors and hydrogen). In some cases, a backup generator (usually powered by fossil fuel, diesel or gasoline) is part of the hybrid system. Low-power standalone systems are usually called off-grid systems and typically power single households by diesel generators or by solar photovoltaic (PV) systems (solar home systems) [1]. Systems of higher power are called micro- or mini-grids, which can supply several households or even a whole village. Mini- or micro-grids, powered by renewable sources, can be classified as smart grids, allowing information exchange between the consumers and the distributed generation [2]. The modelling of the components, the control of the system and the simulation of the performance of the whole system are necessary to evaluate the system technically and economically. The optimization of the sizing and/or the control is also an important task in this kind of systems..

A regional energy system transition modeling tool for decision support:a case study of the Groningen province in the Northern Netherlands

Regionalized national models are uncommon in energy system modeling and analysis. The regional level is vital because this scale assists in identifying the spatial and energy potentials of renewable energy sources. In addition, spatial planning is crucial for energy system transition and typically occurs on a local to regional level. This study created a regional decision-supporting energy system modeling tool using an existing national integrated energy system model named OPERA. The case study was the Dutch province of Groningen. Four systematic methodological steps were followed. First, a crude regionalization framework was created within OPERA. Second, renewable spatial potential was analyzed by modeling with geographic information system-based tools. Third, a regional decision-support tool was developed by adding a spatial interface to the energy system modeling framework. Fourth, this tool was tested and validated by developing stakeholder-informed scenarios and discussing the outcomes in a stakeholder workshop. Important quantitative outcomes of the tool are regional primary energy supply mixes, secondary energy demand balances, interregional energy flows, and related cost structures. The results showed that energy infrastructure is a crucial component of the total system cost. Onshore wind and biomass can play a significant role in the future regional energy system of Groningen, subject to regional and national policies and public perception. The framework can analyze trade-offs, conflicts, and complementarity between stakeholder opinions and perspectives. The stakeholder interaction process highlighted the importance of the science-policy interface. The method is universal and can be applied to other regional contexts, subject to data availability

PV/Wind Hybrid Energy System, Modeling and Simulation at variable weather conditions

This paper presents a modeling and simulation of a grid-connected wind / PV hybrid power system under variable weather conditions. This system includes a wind turbine system, a PV system that shares a DC bus, and no battery. The paper contains an overview of the hybrid system and some previous studies; it presents a brief overview of each component used for this system. Signal distortion remains the great obstacle when connecting to the grid, so the system architecture and its proposed control are also introduced to reduce the distortion of electrical signals to an acceptable value. A simulation of the system’s operation with specific weather conditions in three different modes was performed using the MATLAB Simulink to describe the effect of these weather conditions on the production of electrical energy. Simulation results show how these weather conditions affect the operation of this hybrid system. An acceptable distortion value of the produced current signals has also been reached. These results present an evaluation of the dynamic performance of this system under the proposed working conditions. It also shows the energy exchange with the grid

Dynamic energy system modeling - interfuel competition

Also issued as a Ph.D. thesis in the Dept. of Electrical Engineering, 1972This work reports the formulation, development, validation, and applications of a medium to long range dynamic model for interfuel competition in the aggregated U. S. The economic cost structure, investment decisions, and physical constraints are included specifically in the supply models for coal, oil, natural gas, and nuclear fuels, as well as in the consuming sectors residential and commercial, industrial processing, transportation and electricity. The model simulates the development of supply, the fuel selection process in the consuming sectors, the depletion of the resources, and resolves these into fuels consumed cost-price trends in the energy markets of the U. S. The validation issue is addressed at length through a number of considerations, including comparing the model performance to past reported behavior of the energy system. it is applied to a series of scenarios or case studies to assess the impact of a variety of technologies, policy considerations, and postulated occurrences on the future energy outlook. Here it is seen the model can be a useful tool, forcing a consistent assessment of possible future trends. The model is useful for depicting the effects of policy or hypothesized changes in our energy economy in a complete system framework

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant

THE PURPOSES AND METHODS OF ENERGY SYSTEM MODELING

U radu je opisana svrha modeliranja energetskog sustava i podjela modela za planiranje s obzirom na različitosti pristupa i metodologije. Pojava modernih računala i razvoj računalnih programa pojednostavnila je korištenje modela za planiranje. Ovakvi modeli koriste snažne matematičke algoritme i baze podataka koji u relativno kratkom vremenu mogu riješiti vrlo složene probleme. To je dalje omogućilo nastanak tzv. E3 modela (engl. energy-ecology-economy) koji imaju mogućnost istodobnog sagledavanja pitanja vezanih za energetiku, ali i za ekologiju i ekonomiju. Posebno su prikazane karakteristike modela MARKAL kroz primjer integriranog povezivanja s drugim modelima za planiranje. Pokazuje se da je primjena optimizacijskog modela MARKAL za planiranje energetskog sustava Republike Hrvatske od velikog značenja s obzirom na potrebne analize energetskog tržišta jugoistočne Europe, korištenja obnovljivih izvora energije, energetsku učinkovitost i trgovine emisijama.This article describes the purpose of energy system modeling and the classification of planning models according to approaches and methodologies. The advent of modern computers and computer programs has simplified the use of planning models. Such models employ powerful mathematical algorithms and databases which can solve highly complex problems in a relatively short time. This has led to energy-ecology-economy (E3) models, which are simultaneously able to consider questions in connection with energy supply, ecology and economics. The characteristics of a MARKAL model are presented separately through an example of integration with other planning models. It is demonstrated that the application of an optimizing MARKAL model for the planning of the energy supply system of the Republic of Croatia is of great significance for the analysis of the energy market of South East Europe, the use of renewable energy sources, energy efficiency and emission tradin

THE PURPOSES AND METHODS OF ENERGY SYSTEM MODELING

U radu je opisana svrha modeliranja energetskog sustava i podjela modela za planiranje s obzirom na različitosti pristupa i metodologije. Pojava modernih računala i razvoj računalnih programa pojednostavnila je korištenje modela za planiranje. Ovakvi modeli koriste snažne matematičke algoritme i baze podataka koji u relativno kratkom vremenu mogu riješiti vrlo složene probleme. To je dalje omogućilo nastanak tzv. E3 modela (engl. energy-ecology-economy) koji imaju mogućnost istodobnog sagledavanja pitanja vezanih za energetiku, ali i za ekologiju i ekonomiju. Posebno su prikazane karakteristike modela MARKAL kroz primjer integriranog povezivanja s drugim modelima za planiranje. Pokazuje se da je primjena optimizacijskog modela MARKAL za planiranje energetskog sustava Republike Hrvatske od velikog značenja s obzirom na potrebne analize energetskog tržišta jugoistočne Europe, korištenja obnovljivih izvora energije, energetsku učinkovitost i trgovine emisijama.This article describes the purpose of energy system modeling and the classification of planning models according to approaches and methodologies. The advent of modern computers and computer programs has simplified the use of planning models. Such models employ powerful mathematical algorithms and databases which can solve highly complex problems in a relatively short time. This has led to energy-ecology-economy (E3) models, which are simultaneously able to consider questions in connection with energy supply, ecology and economics. The characteristics of a MARKAL model are presented separately through an example of integration with other planning models. It is demonstrated that the application of an optimizing MARKAL model for the planning of the energy supply system of the Republic of Croatia is of great significance for the analysis of the energy market of South East Europe, the use of renewable energy sources, energy efficiency and emission tradin

Generating synthetic load profiles of residential heat pumps: a k-means clustering approach

The creation of synthetic heat pump load profiles is essential for energy system modeling and simulations. This paper proposes a methodology to create synthetic heat pump load profiles based on the k-means algorithm and a data set from water-to-water heat pumps from Hamelin, Germany. The quality of the generated load profiles is shown according to load factors, load distribution curves and the Pearson correlation coefficient, and is also applied on two exemplary geographies in Germany. We publish our work open-source and provide a web-based heat pump load profile generator

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work