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Trends and developments in the renewable energy sector in Ukraine

The government of Ukraine has adopted the Renewable Energy Directive (RED) with clear goals and a roadmap to facilitate its energy transition towards renewable sources. This is done because of both climate concerns as well as reasons related to Ukraine’s foreign policy which led the government to decide that Ukraine should work more on its own energy independence. Currently the percentage of renewable energy sources in Ukraine is among the lowest of the entire Europe and there is only slow development in terms of the growth of the sector, even though there is a lot of available biomass, given the large and flat surface of the country with a well-developed agricultural sector. As in most countries in the world, there is a quite intensive and well-developed debate in Ukraine about the energy sector, energy usage and the necessary transition towards more renewable types of energy. One of the consequences of it is that Ukraine is one of the partner countries in the Paris agreement and committed itself to reducing the amount of greenhouse gas emissions in the future. That means that a transformation towards renewable energy is needed, even though currently in Ukraine only a low percentage of energy is generated by sustainable sources. The general picture is that in Ukraine the development of the renewable energy sector is going not as fast as could have been. In other words, there are several barriers present that hinder the energy transition. One of the issues behind such a barrier may be a limited access to technology, or problems with legislation or other issues which may be unknown so far, but certainly relevant for foreign investors. The Ukrainian government adopted the so-called Renewable Energy Directive (RED), set goals for the energy transition and support the transition itself. In some areas progress was made, for example in the growing number of biomass fired boilers, but still Ukraine remains one of the European countries with the lowest percentage of renewable energy production. Therefore, in order to identify currently existing barriers and help to find possible applications of new technologies in Ukraine, the Dutch Enterprise Agency (Rijksdienst voor Ondernemerschap) commissioned this study. It was done within the framework of the Partners in Business on Bioenergy program. The focus of this study is on analysing the renewable energy sector, with special attention for biomass, in the form of biomass-based heating and biomass for biofuels. Of course, other parts of the renewable energy sector such as solar and wind energy are also taken into consideration. The second part consists of a case study to determine the business case for direct processing of sugar beets with Betaprocess as a possible application of biomass to biofuel production in Ukraine. The third study is aiming at determining the amount of biomass that can safely be taken from the fields, without negatively affecting the fertility of the soil. These sub-studies mentioned in the previous paragraph offer a better understanding of the renewable energy market in general and biomass/biofuel applications in particular. This study sheds light on several important questions that entrepreneurs and/or other foreign investors may have about investing in Ukraine. Even though it is well-known that doing business in Ukraine is challenging, it is also very important to have a clear picture of the opportunities that this country offers, within the limits that nature sets, in order to avoid negative consequences like soil degradation. The objective of this report is to find out about which opportunities and barriers exist in the Ukrainian transition towards renewable energy generation, to calculate the profitability of new biomass-processing technologies as well as finding out limitations of biomass usage.

The role of perceived contribution to the energy transition in acceptance of renewable energy sources

Building and building systems energy prediction models to enhance energy flexibility control

The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.

Empowering Prosumers to Become Steerers in Local Energy Systems

There is an ongoing transition towards renewable energy sources in order to combat climate change. National power grids are suffering due to the rapid introduction of new energy sources and have other disadvantages. Local Energy Systems (LESs) are a beneficial example of an off-grid energy systems that can aid the energy transition. LESs are community driven and require participating and steering members. This can be achieved through empowering end-users to become active participants or steerers. End-users can be empowered to become an active participant through engagement with energy management activities. This does not work for empowering to steer, which begs the question, how to empower end-users or participants to become steerers in Local Energy Systems. Through a literature review this study explores the importance of establishing a group containing steerers with diverse skills, strong leadership, and engagement with the environment and community. Additionally, this study identifies the strategy that empowers end-users to steer. Which is training technological and managemental skills; and training capabilities in establishing relations with local participants and intermediary organisations. To apply these findings more precisely a secondary analysis is conducted on a survey with 599 participants. The original study researched willingness to participate in LESs, however the secondary analysis establishes three important factors to predict willingness to steer. These are energy independence, community trust, and community resistance. Additionally, men with a high level of education are most willing to become steerers per default, thus different demographics generally require more empowerment.

Role of lng in an optimized hybrid energy network

The future energy system could benefit from the integration of independent gas, heat and electricity infrastructures. Such a hybrid energy network could support the increase of intermittent renewable energy sources by offering increased operational flexibility. Nowadays, the expectations on Natural Gas resources forecast an increase in the application of Liquefied Natural Gas (LNG), as a means of storage and transportation, which has a high exergy value. Therefore, we analyzed the integration of decentralized LNG regasification with a Waste-to-Energy (W2E) plant for a practice-based case to get an idea on how it might affect the balancing of supply and demand, under optimized exergy efficient conditions. We compared an independent system with an integrated system that consists of the use of the LNG cold to cool the condenser of the W2E plant, as well as the expansion of the regasified LNG in an expander, using a simplified deterministic model based on the energy hub concept. We use the hourly measured electricity and heat demand patterns for 200 households with 35% of the households producing electricity from PV according to a typical measured solar insolation pattern in The Netherlands. The results indicate that the integration affects the imbalance for electricity and heat compared to the independent system. If the electricity demand is met, both the total yearly heat shortage and heat excess are reduced for the integrated system. If the heat demand is met, the total yearly electricity shortage is also reduced (with 100 MWh). However, the total yearly electricity excess is then increased (with 300 MWh). We observed that these changes are solely due to the increase in exergy efficiencies for heat and electricity of the W2E Rankine cycle. The efficiency of the expander is too low to offer a significant contribution to the electricity demand. Therefore, future research should focus on the affect that can be obtained by to other means of integration (e.g. Organic Rankine Cycle and Stirling Cycle).

Design of wind and solar energy supply, to match energy demand

Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.