Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India

An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper

A survey of Multi-Criteria Decision Making Technique used in Renewable Energy Planning

Fossil based oil, gas and coal reserves will exhaust in few decades and the accelerated demand for conventional energy have forced planners and policy makers to look for alternate sources of Energy. Renewable energies option serves as a solutions for a sustainable, environmentally friendly and long-term cost effective sources of energies to meet our ever increasing needs of energy.  Renewable energy sites selection can be viewed as a Multiple Criteria Decision Making (MCDM) problem. MCDM is a complex Decision Making (DM) tools as it involves both quantitative and qualitative criteria. In recent years, several MCDM techniques and approaches have been suggested to solve energy planning problems. The main objective of this paper is to systematically review MCDM techniques and approaches in sustainable and renewable energy planning problems. A review of more than 100 published papers based on MCDM analysis is studied and presented in this paper. Findings of this review paper confirm that MCDM techniques can assist stakeholders and decision makers in unravelling some of the uncertainties inherent in renewable energy decision making. Classification of methodology used, criteria selection and application area are summarized and presented

GIS-BASED SUITABILITY MODELING AND MULTI-CRITERIA DECISION ANALYSIS FOR UTILITY SCALE SOLAR PLANTS IN FOUR STATES IN THE SOUTHEAST US

Photovoltaic (PV) development shows significantly smaller growth in the Southeast U.S., than in the Southwest; which is mainly due to the low cost of fossil-fuel based energy production in the region and the lack of solar incentives. However, the Southeast has appropriate insolation conditions (4.0-6.0 KWh/m2/day) for photovoltaic deployment and in the past decade the region has experienced the highest population growth for the entire country. These factors, combined with new renewable energy portfolio policies, could create an opportunity for PV to provide some of the energy that will be required to sustain this growth. The goal of the study was to investigate the potential for PV generation in the Southeast region by identifying suitable areas for a utility-scale solar power plant deployment. Four states with currently low solar penetration were studied: Georgia, North Carolina, South Carolina and Tennessee. Feasible areas were assessed with Geographic Information Systems (GIS) software using solar, land use and population growth criteria combined with proximity to transmission lines and roads. After the GIS-based assessment of the areas, technological potential was calculated for each state. Multi-decision analysis model (MCDA) was used to simulate the decision making method for a strategic PV installation. The model accounted for all criteria necessary to consider in case of a PV development and also included economic and policy criteria, which is thought to be a strong influence on the PV market. Three different scenarios were established, representing decision makers\u27 theoretical preferences. Map layers created in the first part were used as basis for the MCDA and additional technical, economic and political/market criteria were added. A sensitivity analysis was conducted to test the model\u27s robustness. Finally, weighted criteria were assigned to the GIS map layers, so that the different preference systems could be visualized. As a result, lands suitable for a potential industrial-scale PV deployment were assessed. Moreover, a precise calculation for technical potential was conducted, with a capacity factor determined by the actual insolation of the sum of each specific feasible area. The results of the study showed that, for a utility-scale PV utility deployment, significant amount of feasible areas are available, with good electricity generation potential Moreover, a stable MCDA model was established for supporting strategic decision making in a PV deployment. Also, changes of suitable lands for utility-scale PV installations were visualized in GIS for the state of Tennessee

Demand Reduction and Responsive Strategies for Underground Mining

This thesis presents a demand reduction and responsive strategy for underground mining operations. The thesis starts with a literature review and background research on global energy, coal mining and the energy related issues that the mining industry face everyday. The thesis then goes on to discuss underground mine electrical power systems, data acquisition, load profiling, priority ranking, load shedding and demand side management in mining. Other areas presented in this thesis are existing energy reduction techniques, including: high efficiency motors, motor speed reduction and low energy lighting. During the thesis a data acquisition system was designed and installed at a UK Coal colliery and integrated into the mines existing supervisory control and data acquisition (SCADA) system. Design and installation problems were overcome with the construction of a test meter and lab installation and testing. A detailed explanation of the system design and installation along with the data analysis of the data from the installed system. A comprehensive load profile and load characterisation system was developed by the author. The load profiling system is comprehensive allows the definition of any type of load profile. These load profiles are fixed, variable and transient load types. The loads output and electrical demand are all taken into consideration. The load characterisation system developed is also very comprehensive. The LC MATRIX is used with the load profiles and the load characteristics to define off-line schedules. A set of unique real-time decision algorithms are also developed by the author to operate the off-line schedules within the desired objective function. MATLAB Simulation is used to developed and test the systems. Results from these test are presented. Application of the developed load profiling and scheduling systems are applied to the data collected from the mine, the results of this and the cost savings are also presented

STRATEGIC RISK MANAGEMENT FOR TIDAL CURRENT AND WAVE POWER PROJECTS

Tidal current and wave power, as emerging forms of renewable generation, represent innovations that are confronted by significant technological and financial challenges. Currently, the marine energy sector finds itself in a decisive transition phase having developed full-scale technology demonstrators but still lacking proof of the concept in a commercial project environment. After the decades-long development process with larger than expected setbacks and delays, investors are discouraged because of high capital requirements and the uncertainty of future revenues. Although ideas for improving the investment climate can be found, there is a lack of well-founded arguments and coordinated strategies to work towards a breakthrough in the marine energy market. The objective of this research is to provide stakeholder-specific prioritised strategy options for de-risking the commercialisation of tidal current and wave power technologies. A key principle applied is to integrate a wide knowledge spectrum comprising the technology, policy and financing sectors and to compile the information in a holistic and transparent manner. To gain a broad understanding of the characteristics of presently ongoing marine energy activities and the correlated strategic planning, a comprehensive survey was conducted. Based on this multidisciplinary attempt, an all-encompassing appraisal was possible by avoiding over-concentration on stakeholder-specific views or interests. System dynamics modelling was employed to develop a series of cause-effect relationship diagrams of the key interactions and correlations in the field. It was revealed that the circular relationship between two major risks for array-scale projects – reliability and funding – requires coordinated action to overcome. As funding is necessary for improving system reliability (and vice-versa), showcasing “array-scale success” was identified as the game-changing milestone towards commercial generation. Furthermore, it was found that a number of comparably competent manufacturing firms is required to implement major marine energy projects. This would result from fostering a multi-company market breakthrough concept, based on intensified knowledge sharing and trustful collaborative interaction between competitors. Additionally, effective separation of complexity into “detail” and “dynamically complex” constituents was found to be fundamental for identifying long-term, effective solutions. It is decisive to accept this primary classification, as measures appropriately applied on one type of complexity can be counterproductive if applied on the other. Most of the available planning tools and analytical methods do not address the management of dynamic complexity, necessary in innovative environments where flexibility and tolerance of vagueness are indispensable. Successful application of several strategies to deal with both types of complexity in comparable innovation-driven environments was considered suitable for de-risking the commercialisation of marine energy. The challenges for strategy-finding in a demandingly complex and increasingly dynamic environment are addressed in this research by exploiting a case-specific expert knowledge database. The structured information compression and subsequent strategy-finding process is realised based on calculated rankings of impact factors by systems dynamics software and substantiated by representative interview statements. The analysis makes use of multi-level expert knowledge and the application of a control-loop-based methods. The systems approach as applied in this research comprises the combination of interview-based (bottom-up learning) processes and the application of prioritised strategy options in the form of concerted management action (top-down planning). The approach of processing multi-level interview data by system dynamics modelling represents a powerful method to detect and assess ongoing developments and thus to advance strategy-finding. The systematic and unbiased approach to identify the top-level drivers for commercialising marine energy supports the long-term creation of investor confidence, based on a concept of transparency and credibility

Strategic risk management for tidal current and wave power projects

Tidal current and wave power, as emerging forms of renewable generation, represent innovations that are confronted by significant technological and financial challenges. Currently, the marine energy sector finds itself in a decisive transition phase having developed full-scale technology demonstrators but still lacking proof of the concept in a commercial project environment. After the decades-long development process with larger than expected setbacks and delays, investors are discouraged because of high capital requirements and the uncertainty of future revenues. Although ideas for improving the investment climate can be found, there is a lack of well-founded arguments and coordinated strategies to work towards a breakthrough in the marine energy market. The objective of this research is to provide stakeholder-specific prioritised strategy options for de-risking the commercialisation of tidal current and wave power technologies. A key principle applied is to integrate a wide knowledge spectrum comprising the technology, policy and financing sectors and to compile the information in a holistic and transparent manner. To gain a broad understanding of the characteristics of presently ongoing marine energy activities and the correlated strategic planning, a comprehensive survey was conducted. Based on this multidisciplinary attempt, an all-encompassing appraisal was possible by avoiding over-concentration on stakeholder-specific views or interests. System dynamics modelling was employed to develop a series of cause-effect relationship diagrams of the key interactions and correlations in the field. It was revealed that the circular relationship between two major risks for array-scale projects – reliability and funding – requires coordinated action to overcome. As funding is necessary for improving system reliability (and vice-versa), showcasing “array-scale success” was identified as the game-changing milestone towards commercial generation. Furthermore, it was found that a number of comparably competent manufacturing firms is required to implement major marine energy projects. This would result from fostering a multi-company market breakthrough concept, based on intensified knowledge sharing and trustful collaborative interaction between competitors. Additionally, effective separation of complexity into “detail” and “dynamically complex” constituents was found to be fundamental for identifying long-term, effective solutions. It is decisive to accept this primary classification, as measures appropriately applied on one type of complexity can be counterproductive if applied on the other. Most of the available planning tools and analytical methods do not address the management of dynamic complexity, necessary in innovative environments where flexibility and tolerance of vagueness are indispensable. Successful application of several strategies to deal with both types of complexity in comparable innovation-driven environments was considered suitable for de-risking the commercialisation of marine energy. The challenges for strategy-finding in a demandingly complex and increasingly dynamic environment are addressed in this research by exploiting a case-specific expert knowledge database. The structured information compression and subsequent strategy-finding process is realised based on calculated rankings of impact factors by systems dynamics software and substantiated by representative interview statements. The analysis makes use of multi-level expert knowledge and the application of a control-loop-based methods. The systems approach as applied in this research comprises the combination of interview-based (bottom-up learning) processes and the application of prioritised strategy options in the form of concerted management action (top-down planning). The approach of processing multi-level interview data by system dynamics modelling represents a powerful method to detect and assess ongoing developments and thus to advance strategy-finding. The systematic and unbiased approach to identify the top-level drivers for commercialising marine energy supports the long-term creation of investor confidence, based on a concept of transparency and credibility