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Changing the fuel mix used in the heating industry, i.e., switching to greener fuels, is one of the possible solutions to prevent rising costs for final consumers in the context of rising emission allowance prices. This paper presents a methodology that offers the possibility to perform a comprehensive technical and economic assessment of a theoretical solution—changing the fuel mix of centralized heating sources—and other strategic decisions within a district's heating systems. Emphasis is placed on fuels with a negative price, such as municipal waste. The presented approach can also be used to assess the effect of other significant changes related to the configuration of district heating systems on the economy of the plant, such as the impact of a decrease in heat demand and implementation of a steam turbine. The key benefit of this paper is an approach based on mathematical modelling of the operation of individual boilers with different operating parameters in terms of their start-up, shutdown, and mode of operation. A unique approach of optimizing an operation's schedule using dynamic programming is presented, which enables the selection of a suitable solution for the configuration of binary variables in consecutive time steps. In this way, it is possible to achieve a more accurate estimate of the economics of the facility at the strategic planning stage that will consider the real operational capabilities of the heat source given its technical limitations. Using this approach, up to a 4% reduction in variable operating costs was achieved in the model case, when compared to static time interval planning. [ABSTRACT FROM AUTHOR]

Abstract The paper deals with the issue of fluctuations in heat demand and how they are dealt with when planning investments in the field of energy recovery of waste. The lifetime of Waste to Energy plants (WtEP) is typically 20–30 years. Their construction is also a time and investment-intensive business and requires a robust design with low sensitivity to future changes in key parameters. The paper analyses the effect of fluctuations in heat demand on the accuracy of techno-economic models and their outputs. The aim is to determine the sensitivity of optimisation models to the applied time step. The sensitivity of two different models is compared – a simple model of a WtEP cooperating with a gas boiler and a complex model of a WtEP integrated with a combined heat and power plant. Optimisation models, commonly used to design these facilities, perform calculations in certain time steps, typically on an annual or monthly basis. The simplification from hour to month time step may cause inaccuracies. The paper offers alternative solutions to modelling methods of WtEP, using so-called correction coefficients. They help to increase the accuracy of the models while maintaining acceptable calculation time. Highlights • Tracking short-term changes to important parameters for the WtE plant economy. • Modelling the operation of an existing and newly considered thermal facility. • Creating and applying correction coefficients for fluctuation parameters. [ABSTRACT FROM AUTHOR]

In Europe, most of the district heat is produced from fossil energy sources such as coal or natural gas. Together with the intention to divert municipal waste from landfill, which is still the main way of treatment in approximately half of the EU countries, there is a potential for the construction of waste-to-energy plants (WtEP) that can partially replace fossil fuels. An important factor in planning the construction of new WtE plants is the assessment of the project's economy. However, the ecological aspect of the project, namely the greenhouse-gas (GHG) savings, is becoming a topic of discussion nowadays. In terms of GHG savings, WtE plant integration into an existing district heating system (DHS) has a positive impact, but its level is often only roughly estimated. The paper presents a comprehensive mathematical optimization tool working on a daily time interval that is able to evaluate the impact of changing current technology or WtE plant construction on global warming potential. The technical parameters such as boiler output range, energy efficiency, etc. are considered. The tool also allows to put in the context the GHG savings and economic benefits of the project. These two factors are very difficult to compare. The study answers the question of how to deal with this issue and presents the possibility of comparison, which is generally transferable to any two parameters. Due to two poorly comparable criteria, the result does not include a single optimal solution, but clearly illustrates the view of the overall issue and gives a good basis for the final decision. The method used are illustrated in real-world DHS with various parameters where the relationship between GHG savings and the economic benefits of the integration of a WtE plant is evaluated. The whole problem is the task of linear integer programming and is implemented in the GAMS programming environment. • A multi-criteria optimization tool for WtE plant integration is introduced. • Economic and environmental aspects are put into context. • The optimal capacity is determined depending on the emphasis on both aspects. • The cost of removing a tonne of CO 2 by WtE in a particular location is calculated. [ABSTRACT FROM AUTHOR]

Municipal solid waste treatment leads to the production of a considerable amount of mixed municipal waste, in case of which material recovery is difficult. Its treatment represents a worldwide challenge since landfilling is still a major treatment method and the respective emissions of greenhouse gases are significant. Approximately 126 Mt of municipal solid waste were landfilled or incinerated within the EU-28 in 2017, while the waste management sector produced 3% of the overall greenhouse gases emissions. Regarding mixed municipal waste, Waste-to-Energy plants seem to be a suitable disposal option as they substitute both landfills and energy production from fossil fuels in combined heat and power plants. However, new treatment facilities of this type need to take into account also the heat and electricity demands in their vicinity to ensure economic stability. This paper therefore analyses the relationship between greenhouse gases emissions and the cost of mixed municipal waste treatment, while considering environmental impact of different treatment options. A reverse logistic (mixed integer programming) model has been developed to optimise future strategies of mixed municipal waste treatment in a large geographical area. The model is nonlinear because of the nonlinear nature of the cost of mixed municipal waste treatment as well as the economic incentive associated with the avoided greenhouse gases emissions. These, in turn, are influenced by plant capacities, locations, and other location-specific parameters (such as the yearly heat demand profile) that must be considered during the integration of a future plant into the existing district heating systems. The results are presented through a case study for the Czech Republic, with 206 micro-regions (waste producers), 148 landfills, 113 potential mechanical-biological treatment plants, 24 potential locations for plants utilising refuse-derived fuels, 4 existing Waste-to-Energy plants, and 32 candidate locations for new Waste-to-Energy plants have been considered. The proposed future concepts involving various processing chains (small Waste-to-Energy plant, large Waste-to-Energy plant with the necessary complex logistics, mechanical biological treatment prior to incineration), are compared with the current (2016) waste treatment strategy, in which 73% of mixed municipal waste is landfilled. The trade-off between economically viable and environmentally acceptable solution is also targeted. The obtained data suggest a possible reduction in greenhouse gases emissions by almost 150% with the cost of waste treatment being increased only by approx. 2.5 EUR/t. Image 1 • Cost of reducing greenhouse gas emissions is evaluated using the proposed method. • Emissions of the Waste-to-Energy strategy are assessed and implemented in model. • Locations of new plants are suggested to reduce greenhouse gases at a viable cost. • Reduction in greenhouse gases by 150% causes only 2.5 EUR/t increase in the cost. [ABSTRACT FROM AUTHOR]