• Energy Research

This article provides an agent-based model of a hypothetical standalone electricity network to identify how the feed-in tariffs and the installed capacity of wind power, calculated in percentage of total system demand, affect the electricity consumption from renewables. It includes the mechanism of electricity pricing on the Day Ahead Market (DAM) and the Imbalance Market (IM). The extra production volumes of Electricity from Renewable Energy Sources (RES-E) and the flexibility of electrical consumption of industries is provided as reserves on the IM. Five thousand simulations were run by using the agent-based model to gather data that were then fit in linear regression models. This helped to quantify the effect of feed-in tariffs and installed capacity of wind power on the consumption from renewable energy and market prices. The consumption from renewable sources, expressed as percentage of total system consumption, increased by 8.17% for every 10% increase in installed capacity of wind power. The sharpest increase in renewable energy consumption is observed when a feed-in tariff of 0.04 €/kWh is provided to the wind farm owners, resulting in an average increase of 9.1% and 5.1% in the consumption from renewable sources while the maximum installed capacity of wind power is 35% and 100%, respectively. The regression model for the annualized DAM prices showed an increase by 0.01 €cents/kWh in the DAM prices for every 10% increase in the installed wind power capacity. With every increase of 0.01 €/kWh in the value of feed-in tariffs, the mean DAM price is lowered as compared to the previous value of the feed-in tariff. DAM prices only decrease with increasing installed wind capacity when a feed-in tariff of 0.04 €/kWh is provided. This is observed because all wind power being traded on DAM at a very cheap price. Hence, no volume of electricity is being stored for availability on IM. The regression models for predicting IM prices show that, with every 10% increase in installed capacity of wind power, the annualized IM price decreases by 0.031 and 0.34 €cents/kWh, when installed capacity of wind power is between 0 and 25%, and between 25 and 100%, respectively. The models also showed that, until the maximum installed capacity of wind power is less than 25%, the IM prices increase when the value of feed-in tariff is 0.01 and 0.04 €/kWh, but decrease for a feed-in tariff of 0.02 and 0.03 €/kWh. When installed capacity of wind power is between 25 and 100%, increasing feed-in tariffs to the value of 0.03 €/kWh result in lowering the mean IM price. However, at 0.04 €/kWh, the mean IM price is higher, showing the effect of no storage reserves being available on IM and more expensive reserves being engaged on the IM. The study concludes that the effect of increasing installed capacity of wind power is more significant on increasing consumption of renewable energy and decreasing the DAM and IM prices than the effect of feed-in tariffs. However, the effect of increasing values of both factors on the profit of RES-E producers with storage facilities is not positive, pointing to the need for customized rules and incentives to encourage their market participation and investment in storage facilities.

To mitigate climate change, global greenhouse gas emissions need to be reduced substantially. Industry and energy sector together are responsible for a major share of those emissions. Hence the development of low carbon business parks by maximising energy efficiency and changing to collective, renewable energy systems at local level holds a high reduction potential. Yet, there is no uniform approach to determine the optimal combination and operation of energy technologies composing such energy systems. However, techno-economic energy models, custom tailored for business parks, can offer a solution, as they identify the configuration and operation that provide an optimal trade-off between economic and environmental performances. However, models specifically developed for industrial park energy systems are not detected in literature, so identifying an existing model that can be adapted is an essential step. In this paper, energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, main model features are compared, a practical typology is proposed and applicability towards modelling industrial park energy systems is evaluated. Energy system evolution models offer the most perspective to compose a holistic, but simplified model, whereas advanced energy system integration models can adequately be employed to assess energy integration for business clusters up to entire industrial sites. Energy system simulation models, however, provide deeper insight in the system’s operation.

Abstract Process industries are the foundation of the European economy, transforming raw materials into building blocks for strategic products and applications in today's society. Such industries range from steel, cement, or minerals to chemicals such as lubricants for wind turbines and polymers that prevent waste in logistic supply chains. The downside of this foundation industry is its high environmental impact regarding emissions, and intensive use of energy and resources. One of the key strategies to address such challenges is industrial symbiosis: various industries establish collective efforts to find value while transitioning to a more circular economy. This paper presents an exploratory analysis of databases on IS case studies. We used the European standard classification for economic activities (NACE) to draw industrial sector profiles for the most relevant energy-intensive industries: chemicals, steel, and cement, coupled with urban synergies. The majority of the synergies includes the chemicals sector with most commonly shared streams being energy, water, and carbon dioxide. IS cases are ranked in terms of frequency, then classified in topical groups and finally, the sustainability impact of the different categories is discussed. The outcome is a methodology to frame and assess industrial symbiosis case collections useful for future exploring and exploiting circularity projects in public and private organisations.

Similar to many other Western countries, Belgium has committed to internationally set climate goals, such as the reduction in primary energy consumption and the increase in the share of renewable energy production in the total energy mix. Additionally, Belgium has decided to phase out its nuclear energy production, the nation’s largest source of low carbon electricity. In this paper, the role of Belgian business parks and industrial clusters in contributing to the climate goals is investigated, based on the experiences of the authors on several business parks and industrial clusters. The concepts of cogeneration, advanced thermal grids, and local energy communities are discussed and applied on pilot clusters. Their effectiveness towards achieving the climate goals is evaluated, and finally, some policy recommendations are proposed. The results are based on the Belgian situation but are valid for other countries facing similar challenges.

For the last 20 years, the field of industrial symbiosis (IS) has raised interest among academics and industries. IS consists of dissimilar entities sharing and valorising underutilised resources such as materials, energy, information, services, or technologies in the view of increasing the industrial system’s circularity. Despite the benefits brought by IS, though, barriers hindering the full dissemination of IS remain. This paper presents a methodology developed in the framework of the H2020 European project EPOS that aims at removing some of the obstacles to the implementation of IS. The method follows a multidisciplinary approach that intents to trigger the interest of industry decision-makers and initiate efforts to optimise the use of energy and material resources through symbiosis. It is applied to an industrial cluster located in the Humber region of UK. The case study shows how the approach helped to identify several IS opportunities, how one particular high-potential symbiosis was further assessed, and how it led to the creation of a business case. It was estimated that the identified symbiosis could bring substantial economic (+2000 k€ pa), environmental (−4000 t of CO2 eq. pa) and social (+7 years of healthy life) gains to the region.

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium).