Bayesian network development and validation for siting selection

In this study, increasing electricity demand requires considerable attention to increasing the diversity of power generation. Alternative energy can produce heating and power systems and thermal storage. Our objective and every organization’s objectives are to minimize its energy consumption cost under electricity demand uncertainty. In rural areas, heat and power availability and stability are also crucial. Combined heat and power have proven their effectiveness as a subsequent to Electricity. This paper identified four criteria and eleven sub-criteria to determine the most appropriate structure location for combined heat and power in the rural community. The Bayesian Network technology has been applied to analyze these criteria comprehensively. A case study including multiple sites across the Mississippi state was used to validate the proposed approach, and propagation and sensitivity analysis were used to evaluate performance. Results showed the summarized eleven criteria proposed Bayesian Network approach could aid location selection for Combined heat and power location in the rural area. Supplementary, the created model can support decision-makers to select the best alternatives under different electricity demand variability levels

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Sustainable biomass power plant location in the Italian Emilia-Romagna region

Biomass power plants are very promising for reducing carbon oxides emissions, because they provide energy with a carbon-neutral process. Biomass comes from trees and vegetables, so they provide a renewable type of energy. However, biomass plants location, along with their provisioning basins, are heavily regulated by economical aspects, often without careful consideration of their environmental footprint. For example, some Italian biomass plants import from overseas palm-tree oil that is economically convenient. However, the energy consumed for the oil transportation is definitely greater than the energy produced by the palm-tree oil burning. In this way biomass power plants turn out to be environmentally inefficient, even if they produce renewable energy. We propose an Integer Linear Programming approach for defining the energy and cost-efficient biomass plant location along with the corresponding provisioning basin. In addition, the model enables to evaluate existing plants and their energy and cost efficiency. Our study is based on real data gathered in the Emilia-Romagna region of Italy. Finally, this optimization tool is just a small part of a wider perspective that is aimed to define decision support tools for the improvement of regional planning and its precise strategic environmental assessment

OPTIMAL USES OF BIOMASS RESOURCES IN DISTRIBUTED APPLICATIONS

Biomass production is spatially distributed resulting in high transportation costs when moving dedicated biomass crops and crop residues. A multifaceted approach was taken to address this issue as the low bulk and energy density of biomass limits transportation efficiency. Two systems were analyzed for the conversion of biomass into a denser feedstock applicable to on-farm use. Pelletization was able to densify the material into a solid fuel. Using a pilot scale flat ring pellet mill, the density of the material was able to be increased to at least 4.4 times that of uncompressed material. Pellet durability was found to be strongly related to the moisture content of the material entering the mill. Unlike with ring roller pellet mills, a higher durability was typically seen forbiomass materials with a preconditioned moisture content of 20% (w.b.). From a liquid fuel standpoint, the conversion of lignocellulosic material into biobutanol on-farm was the second method investigated. For the pretreatment of biomass, alkaline hydrogen peroxide spray was demonstrated to be an effective enhancer of saccharification. The viability of on-farm biobutanol preprocessing bunker facilities within Kentucky was analyzed using Geographic Information systems (GIS) to specifically address transportation related factors. The spatial variability of corn field production, size, and location were resolved by utilizing ModelBuilder to combine the various forms of data and their attributes. Centralized and Distributed preprocessing with Centralized refining (DC) transportation systems were compared. Centralized was defined as transport of corn stover directly from the field to a refinery. Distributed-Centralized was specified as going from the field to the biobutanol bunker with corn stover and from the bunker to the refinery with a dewatered crude biobutanol solution. For the DC design, the location of the field and refinery were fixed with the biobutanol bunker location being variable and dependent upon differing maximum transportation (8-80 km) cutoffs for biomass transport from the field to biobutanol bunkers. The DC designs demonstrated a lower (38 - 59%) total transportation cost with a reduced fuel use and CO2 emissions compared to the centralized system

A GIS-Multicriteria Approach to Analyzing Noise and Visual Impacts of Wind Farms

Land-use conflicts in facility siting can trigger public opposition in communities. A negative public perception, such as the Not-in-my-backyard (NIMBY) attitude, is a planning issue that is strongly associated with some types of siting decisions. After the Feed-in-Tariff (FIT) program through the Green Energy Act was introduced in Ontario in 2009, a large number of wind farm developments were proposed and implemented. Public concerns regarding the noise and aesthetic impacts of wind turbines have created public resistance and caused project delays. More importantly, the wind farm siting decision making process is a top-down process, which overrides the power of municipalities and ignores public concerns towards wind farms. In this thesis, a Geographic Information System (GIS)-based multi-criteria decision analysis (MCDA) siting approach has been developed, which is capable of representing the potential noise and visual impacts caused by wind turbines in a wind farm siting process. After identifying a sample of feasible sites in Southern Ontario, the noise and visual impact assessment approaches were applied to estimate the affected-population by wind farm sites. The changes of suitability levels within each feasible site can be determined after the integration of noise and visual criteria with the common siting criteria, which include physical, environmental, planning and economic factors. This siting approach is generalizable, which means it can be applied to other facility developments that have potential noise and visual impacts to the public. The results illustrate the spatial changes of suitability level before and after introducing the noise and visual criteria into the siting process. Planners and decision makers could potentially apply this siting approach to address public concerns in the future wind farm siting decisions

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes