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A systems perspective of waste management allows an integrated approach not only to the five basic functional elements of waste management itself (generation, reduction, collection, recycling, disposal), but to the problems arising at the interfaces with the management of energy, nature conservation, environmental protection, economic factors like unemployment and productivity, etc. This monograph separately describes present practices and the problems to be solved in each of the functional areas of waste management and at the important interfaces. Strategies for more efficient control are then proposed from a systems perspective. Systematic and objective means of solving problems become possible leading to optimal management and a positive contribution to economic development, not least through resource conservation. India is the particular context within which waste generation and management are discussed. In considering waste disposal techniques, special attention is given to sewage and radioactive wastes.

The purpose of this paper is to outline an integrative modelling approach that includes agricultural and forestry process chains in an energy system model, on a regional scale. The main focus is on land use for biomass production, aimed at satisfying the demands for energy, food, and materials.

The described model combines geographic modelling with a linear optimisation approach. The cost‐based optimisation of the energy system includes agricultural and forestry process chains. The system's commodities and processes are identified and these are linked appropriately in the specifications of the reference system. Spatial models provided geographically specific input data for the optimisation; these spatial models were based on publicly available data, regional heat and electricity demands, and regional biomass potentials. The optimisation tool was applied in two case studies.

The optimisation results allow an improved understanding of the interdependencies between regional agricultural and forestry structures and the regional energy system. Future developments of the energy system can be quantified. The application of the model in the case studies has revealed the limits on biomass availability, even in rural areas, and the fossil fuel price sensitivity of an optimal system setup.

Geographic models linked to a forecast model approach and based on publicly available data allow a high spatial resolution by taking into account the region‐specific conditions and mean that the modelling approach is transferrable to other regions. This paper provides an initial insight into the linkage between bottom‐up optimisation and spatial modelling, representing an innovative approach that is yet to be well explored.

The purpose of this paper is to provide a comparative overview of existing energy system models to see whether they are suitable for analysing energy, environment and climate change policies of developing countries.

The paper reviews the available literature and follows a systematic comparative approach to achieve its purpose.

The paper finds that the existing energy system models inadequately capture the developing country features and the problem is more pronounced with econometric and optimisation models than with accounting models.

Inaccurate representation of energy systems in the models can lead to inaccurate decisions and poor policy prescriptions. Thus, the paper helps policy makers and users to be aware of the possible pitfalls of various energy system models.

The purpose of the work is to elaborate a model framework that includes location related temporal characteristics in energy supply and demand. These characteristics in mind an imaginable energy system setup can be explored with the framework. In a case study the possible coverage of the global energy demand, by solar‐ and wind power in junction with a backup technology is treated.

Spatially and temporally high disaggregated data describing different aspects of the energy supply side (especially devoted to renewable resources and related availabilities) as well as the energy demand side are investigated. This information is processed to serve as input for the TIMES model generator in a special adapted model. The complete workflow is enclosed in a graphical user interface implemented as a plugin in the software package ArGIS.

The elaborated case study shows the practicability of the approach to treat spatially and temporally high disaggregated problems in the energy system. Especially sensibilities of an optimal system setup in dependency on assumptions on specific costs for energy transport or storage can be investigated in a very detailed manner.

Since the spatial and temporal disaggregated examination implies the treatment of huge datasets, simplifications have to be made in the description of the technological setup of the energy system. The approach is appropriate to describe single scenario set‐ups but not a complete forecast based system development.

Geographic information systems (GIS) and geographic information are tied together with a conventional modeling approach of energy systems. That enables the cognition and quantification of influences and sensibilities related to spatial and temporal deviations in our energy system either on the supply or the demand side.

Energy scenarios have long been successfully used to inform decision-making in energy systems planning, with a wide range of different methodological approaches for developing and evaluating them. The purpose of this study is to analyze the existing approaches and classify them with a morphological box.

This paper builds upon the methodological literature on developing and evaluating energy scenarios and presents a morphological box, which comprises parameters describing the scenario properties, (energy system) model properties, scientific practice and institutional settings of energy scenarios. The newly developed morphological box is applied to four selected energy scenarios of the German energy transition.

The morphological box is a suitable tool to classify current energy scenarios. The exemplary application also points toward four challenges in the current practice of energy scenario development and evaluation: increasing complexity of decision problems, transparency of the scenario development process, transparency of the decision support process and communication of uncertainty.

The morphological box of energy scenarios helps researchers soundly document and present their methodological approaches for energy scenario development and evaluation. It also facilitates the work of analysts who want to classify, interpret and compare energy scenarios from a methodological perspective. Finally, it supports the identification of gaps between current practice and the methodological literature on energy scenarios, leading to the development of new types of energy scenarios.

The objective of this study is to highlight the questions arising in the design of district heating and cooling systems (DHCSs) in a distributed generation context and to present a model to help find cost‐effective solutions.

Literature on energy systems optimisation is reviewed and a mixed integer programming model for decentralized DHCSs design is developed and applied to two real case studies.

Distributed cooling generation partly coupled with distributed cogeneration and DH is the preferred solution in the examined areas. The optimal configurations, with special reference to network sizing and layout, significantly depend on heating demand profiles and energy prices.

Interdependencies between energy units sizing and network layout definition should be considered. Obtaining more robust and reliable network configurations should be the objective of future modelling efforts.

Despite the growth of distributed energy conversion, designers often rely on centralized concepts in order to reap economies of scale. The presented model helps in discovering less usual solutions representing the most profitable option.

Combining and comparing central and distributed production of heat and cooling under consideration of network costs.

Sustainability is an accepted measure of business performance, with reductions in energy demand a commonly practised sustainability initiative by multinational corporations (MNCs). Traditional energy models have limited scope when applied to the entire MNC as the models exhibit high data and time intensity, high technical proficiency, specificity of application and omission of non-manufacturing activities. The purpose of this paper is to propose a process centric energy model (PCEM), which adopts a novel approach of applying business processes for business energy assessment and optimisation. Business processes are a fundamental requirement of MNCs across all sectors. The defining features of the proposed model are genericity, reproducibility, minimum user input data, reduced modelling time and energy evaluation of non-manufacturing activities. The approach forwards the adoption of Industry 4.0, a subset of which focuses on business process automation or part thereof.

A quantitative approach is applied in development of the PCEM. The methodology is demonstrated by application to the procure to pay and electroplating business processes.

The PCEM quantifies and optimises the business energy demand and associated carbon dioxide emissions of the procure to pay and electroplating business processes, validating the application of business processes. The application demonstrates minimum user inputs as only equipment operational parameters are required and minimum modelling time as business process models and optimisation options are pre-defined requiring only user modification. As MNCs have common business processes across multiple sites, once a business process energy demand is quantified, its inputs are applied as the default in the proceeding sites, only requiring updating. The model has no specialist skills requirement enabling business wide use and eliminating costs associated with training and expert’s services. The business processes applied in the evaluation are developed by the researchers and are not as comprehensive as those in actual MNCs, but is sufficiently detailed to accurately calculate an MNC energy demand. The model databases are not exhaustive of all resources found in MNCs.

This paper provides a new approach to MNC business energy assessment and optimisation. The model can be applied to MNEs across all sectors. The model allows the integration of manufacturing and non-manufacturing activities, as it occurs in practice, providing holistic business energy assessment and optimisation. The model analyses the impacts of the adoption of Industry 4.0 technologies on business energy demand, CO₂ emission and personnel hours.

Earlier most of the research groups have designed and developed hybrid renewable energy system models with technological, scientific and industrial advancement for the energy systems, but slight attention has been paid towards the grid-connected sustainable urban residential energy systems (SUR_eS) for metropolitan cities. The current research wishes to design, model and analyze grid-connected energy system for residential applications for sustainable urban residential energy system. The works aims to explore the potential of the augmented energy system for grid-connected energy system.

The proposed grid-connected SUR_eS are validated for a sample location at New Delhi (India) with a hybrid optimization model for electric renewable (HOMER) software to define and understand the various load profile. It presents the sensitivity analysis approach to validate the design of the proposed energy system.

The obtained results reports the key barriers, proposed model and scenarios for sustainable urban energy system development.

Similar approaches can be replicated to design and develop an independent, self-sustainable cleaner and environmental-friendly energy system in the future scenario for the extension of complex grid infrastructures.

It will assist the stakeholder in solving the complex urban sustainability issues raised due to the shortage of energy.

It will offer a clean and environment friendly sustainable energy resources with reduced carbon emissions. It will benefit sustainable energy resources with a mix of challenges and opportunities, to suggest an approach for implementation of efficient energy policies to optimize the existing and forthcoming energy systems.

The current research offers a design and model to analyze grid-connected energy system sustainable urban residential applications. It explores the potential of the augmented energy system. The proposed model are validated for a sample location with HOMER simulation software to define and understand various scenarios of the multiple load profile. The work presents the sensitivity analysis approach to validate the proposed energy system.

Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.

To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.

To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

With the growing deployment of variable renewable energy sources, such as wind and PV and the increasing interconnection of the power grid, multi-regional energy system models (ESMs) are increasingly challenged by the growth of model complexity. Therefore, the need for developing ESMs, which are realistic but also solvable with acceptable computational resources without losing output accuracy, arises. The purpose of this study is to propose a statistical approach to investigate asynchronous extreme events for different regions and then assess their ability to keep the output accuracy at the level of the full-resolution case.

To extract the extreme events from the residual demands, the paper focuses on analyzing the tail of the residual demand distributions by using statistical approaches. The extreme events then are implemented in an ESM to assess the effect of them in protecting the accuracy of the output compared with the full-resolution output.

The results show that extreme-high and fluctuation events are the most important events to be included in data input to maintain the flexibility output of the model when reducing the resolution. By including these events into the reduced data input, the output's accuracy reaches the level of 99.1% compared to full resolution case, while reducing the execution time by 20 times.

Moreover, including extreme-fluctuation along with extreme-high in the reduced data input helps the ESM to avoid misleading investment in conventional and low-efficient generators.