Optimum sizing of supply equipment for time varying demand

The sizing of supply equipment to meet a time varying demand is an important engineering problem. Optimal sizing of various supply equipment can reduce the overall cost of the supply system significantly. In this paper, the screening curve methodology, originally proposed for planning electrical power system, is extended to address various process system related problems: cost optimal sizing of various pumps to satisfy time varying water demand, ideal mix of various lighting options for a given lighting load, etc. These examples illustrate that the proposed methodology is a simple, versatile, and powerful tool for appropriately sizing various equipment to satisfy time varying demands during grassroots design. During debottlenecking, supply system is expanded; new supply equipment are installed along with appropriate utilisation of existing supply equipment. A methodology is proposed to address expansion planning of various supply equipment during debottlenecking and demonstrated using an example of debottlenecking an air conditioning system. (C) 2015 Elsevier Ltd. All rights reserved

Multiple objectives Pinch Analysis

Pinch Analysis, an algebraic and efficient optimization technique, has been applied to a wide array of problems ranging from water networks to power systems. Primarily, it has been applied to optimize resource conservation problems with single objective. In this paper, techniques of Pinch Analysis are extended to address resource conservation problems with multiple objectives. The concept of prioritized cost, originally proposed for optimizing single objective problems with multiple resources, is extended to address Multiple Objectives Pinch Analysis (MOPA) problems. Multiple objectives prioritized cost (MOPC) provides a trade off between cost and quality of different resources. A prioritizing sequence is proposed to arrange multiple resources in the order of decreasing quality and increasing MOPC. Prioritized sequence helps in solving MOPA problems and generating Pareto optimal front through simplified solution procedure. Applicability of the proposed methodology is demonstrated through two objectives hydrogen and water conservation problems. Additionally, the proposed methodology is applied to analyse the Indian power sector with constraint on emission of carbon dioxide to optimize cost and water footprint of new power plants. (C) 2016 Elsevier B.V. All rights reserved

Multi-objective pinch analysis for power system planning

Given the rising levels of greenhouse gases and the dependence of power generation on fossil fuels, power system planning with emission constraint is of crucial importance. The objective of emission constrained power sector planning is to identify an optimal energy mix, capable of supplying the required amount of electrical energy while simultaneously keeping emissions within a predefined limit. Cost minimisation is the common objective in power sector planning. Additionally, the choice of one power plant over another involves considering a large number of social, environmental, and economic factors. A multi-objective approach is better suited to address such a complex problem. In this paper, Pinch Analysis, a single objective optimisation method, is modified to address multi-objective problems. It is then applied to simultaneously minimise the land footprint, water footprint, and capital cost associated with energy generation for the Indian power sector. A graphical solution space containing all Pareto optimal solutions for a three-objective problem is also presented. It is seen that for India, the energy mix is dominated by photovoltaic and carbon capture enabled coal power plants. The energy mix for least water footprint contains only photovoltaic power plants while that for least land footprint has a mix of wind, nuclear, small hydel, photovoltaic and biomass. Capital investment is the minimum when biomass and nuclear power plants, along with carbon capture enabled coal plants supply the demand, making biomass the only renewable to feature in the cost optimal mix. Existing coal power plants continue to supply over 35% of the energy requirements for the entire solution space. The overall results highlight the importance of solar PV and carbon capture technology. (C) 2017 Elsevier Ltd. All rights reserved

Emission constrained power system planning: a pinch analysis based study of Indian electricity sector

In the light of rising electricity demands and a need to curb carbon dioxide emissions, this article investigates the problem of power system planning with emission targeting. A pinch analysis based approach is utilised here. The key aspect of this study is investigating the parameters that decide the priority of one type of power plant over another. For this, a quantity called prioritised cost, a trade off between cost incurred and emission from a new power plant is identified. In addition to cost and emission factor of a power plant, a third parameter, the present state of the system, also plays a significant role in deciding a power plant's prioritised cost. The analysis done proves that new power plants can be added to the system in the order of their prioritised cost. This methodology is applied to Indian power sector as a case study. Two different problems, involving minimisation of investment and annualised cost, are considered. It is observed that renewables are slightly more favoured when the objective is to minimise overall cost and not just the capital investment. In both cases, the energy mix is still dominated by coal-based power generation. The share of renewables was seen to increase with more stringent emission targets when the objective was to minimise overall cost

Optimization of photovoltaic-thermal (PVT) based cogeneration system through water replenishment profile

A photovoltaic thermal (PVT) system is a renewable cogeneration system that produces low temperature heat and electricity simultaneously from solar radiation. In water based PVT systems, as the thermal load is served, storage tank is replenished immediately with cold makeup water. However, it is possible to determine an optimal water replenishment profile to optimize the overall configuration of PVT system. In this paper, effects of water replenishment on PVT system sizing are studied. At first, the problem is modeled as a mixed integer non-linear programming problem to analyze the impact of water replenishment on PVT system sizing. Subsequently, the design space approach is used to analyze the significance of water replenishment strategy on the PVT system configuration. Finally, based on analytical derivation, an approximate water replenishment profile is determined and its practical implementation strategies are discussed. Applicability of the proposed methodologies is demonstrated through sizing PVT systems for residential as well as industrial hot water usage patterns. Significant reductions in both collector area (10% for residential example and 7% for industrial example) and storage volume (66% for residential example and 23% for industrial example) are observed. (C) 2016 Elsevier Ltd. All rights reserved

Sizing of standalone photovoltaic thermal (PVT) systems using design space approach

A photovoltaic thermal (PVT) system combines photovoltaic cells and thermal collectors and is capable of simultaneously producing low temperature heat and electricity. A complete PVT system requires a thermal as well as an electrical energy storages to meet any mismatch between the demand and generation. The objective of this work is to develop a methodology for effectively sizing a PVT system. A sizing philosophy, called the design space approach, is applied to size the overall system. A design space is the collection of all feasible design configurations. It is observed that the design space for a PVT system is governed by the electrical demand, the thermal demand, the temperature requirement for the thermal load, and the boiling point of working fluid. A sensitivity analysis of the overall system sizing is carried out by varying electrical load, thermal load, and thermal load temperature. (C) 2013 Elsevier Ltd. All rights reserved