Fuel cell systems and traditional technologies. Part I: Experimental CHP approach

One of the most innovative solutions concerning CHP for residential and industrial applications consists in using fuel cell devices. The importance of this technology is connected to the possibility of having a nearly complete energetic independence. A comparison between traditional systems for energy generation and co-generative fuel cell systems is needed to properly evaluate whether fuel cells could be a reasonable alternative to conventional systems.The present work describes the project of an experimental setup which is focused on testing the high temperature Solid Oxide Fuel Cells (SOFC) concept as a promising innovative system. The problem of planning facilities based on fuel cell devices is faced, and the still-to-be-solved question of thermal storage is addressed. The core of the work consists of a theoretical calculation and comparison of fuel consumption for both the fuel cell and traditional systems.     

Power generation with gas turbine systems and combined heat and power

This article gives an overview of power generation with gas turbine and combined heat and power (CHP) systems. It also presents the European Union strategy for developing gas turbines and CHP systems. Ways to improve the performance of the several types of gas turbine cycle will be a major objective in the coming years. The targets are combined cycle efficiencies above 60% industrial gas turbine system efficiencies of at least 50% and small gas turbines efficiencies above 35% and designs for the use of fuels with less than 25% heating value of that of natural gas. The main CHP targets are the reduction of the overall costs and the development of above 40 kW biomass-fired systems.     

Spark spread – A screening parameter for combined heating and power systems

Combined heating and power (CHP) systems may be considered for installation if they produce savings over conventional systems with separate heating and power. For a CHP system with a natural gas engine as the prime mover, the difference between the price of natural gas and the price of purchased electricity, called spark spread, is an indicator as to whether a CHP system might be considered or not. The objective of this paper is to develop a detailed model, based on the spark spread, that compares the electrical energy and heat energy produced by a CHP system against the same amounts of energy produced by a traditional, or separate heating and power (SHP) system that purchases electricity from the grid. An expression for the spark spread based on the cost of the fuel and some of the CHP system efficiencies is presented in this paper as well as an expression for the payback period for a given capital cost and spark spread. The developed expressions allow determining the required spark spread for a CHP system to produce a net operational savings over the SHP in terms of the performance of system components. Results indicate that the spark spread which might indicate favorable payback varies based on the efficiencies of the CHP system components and the desired payback period. In addition, a new expression for calculating the payback period for a CHP system based on the CHP system capital cost per unit of power output and fuel cost is proposed.     

Modelling and optimization of the smart hybrid renewable energy for communities (SHREC)

The future energy system in community level should be more ‘smart’ to secure reliability, enhance market service, minimize environmental impact, reduce costs and improve the use of renewable energy source (RES). Therefore, this paper proposes an energy integration system – smart hybrid renewable energy for communities (SHREC). It considers both thermal (heating and cooling) and electricity market in a large community level and highlight the interactions between them through utilizing RES, combined heat and power (CHP) and energy storages. A planning model based on CHP modelling is developed for the SHREC system. A linear programming (LP) algorithm is developed to optimize the SHREC system in a weekly period and the results are compared with an existing energy optimization software. We also demonstrate the model in a sample SHREC system during three typical weeks with cold, warm and mid-season weather in the year 2011. The results indicate that the developed modelling and optimization method is more efficient and flexible for the smart hybrid renewable energy systems.     

Wind turbines type and number choice using combinatorial optimization

The paper addresses the problem associated with the optimal wind park design. A combinatorial optimization model for wind turbines type and number choice and placement considering the given wind conditions and wind park area is developed. The wind park investment costs and the total power relation as function of wind turbines number and type are used as optimization criteria. The optimization problem is formulated as a single criterion mixed-integer nonlinear discrete combinatorial task. The different wind park conditions are introduced into optimization tasks formulation as variables relations and restrictions. Two basic wind directions cases are taken into consideration – uniform and predominant wind directions for two wind park area shapes – square and rectangular. The developed wind park design approach was tested numerically by solving of different optimization tasks formulations based on wind turbines real parameters data. The numerical testing shows the applicability of the developed optimization approach. Using it will help to find mathematically reasoned wind turbines choice as contradiction to the heuristic approaches.     

Combined heat and power considered as a virtual steam cycle heat pump

The first aim of this paper is to shed light on the thermodynamic reasons for the practical pursuit of low temperature operation by engineers involved in the design and the operation of combined heat and power (CHP) and district heating (DH) systems. The paper shows that the steam cycle of a combined heat and power generator is thermodynamically equivalent to a conventional steam cycle generator plus an additional virtual steam cycle heat pump. This apparently novel conceptualisation leads directly to (i) the observed sensitivity of coefficient of performance of CHP to supply and return temperatures in associated DH systems, and (ii) the conclusion that the performance of CHP will tend to be significantly higher than real heat pumps operating at similar temperatures. The second aim, which is pursued more qualitatively, is to show that the thermodynamic performance advantages of CHP are consistent with the goal of deep, long-term decarbonisation of industrialised economies. As an example, estimates are presented, which suggest that CHP based on combined-cycle gas turbines with carbon capture and storage has the potential to reduce the carbon intensity of delivered heat by a factor of ∼30, compared with a base case of natural gas-fired condensing boilers.     

考虑机组组合问题的机组检修计划优化新模型

以经济费用最小为目标函数,建立了发电机组检修计划优化问题（UMS）新模型。由于生产费用在经济费用中占有的比例最大,因此在计算新模型的生产费用时考虑了发电机组组合优化问题（UC）。鉴于考虑UC问题的UMS问题为双层优化问题,其中UMS问题为上层优化问题,UC问题为下层优化问题,提出了一种改进离散粒子群算法（MDPSO）,并将其用于搜索UMS问题的最优解向量,即解决上层优化问题;而由于拉格朗日松弛法在解决UC问题上具有计算速度快、结果精度高等优点,将其用于解决下层优化问题。利用该新模型和MDPSO算法对IEEE-RTS系统的机组的年检修计划进行优化,并与离散粒子群算法（DPSO）比较,结果表明DPSO算法在解决UMS问题上具有精度高、收敛速度快等优点。     

An artificial neural network‐based condition monitoring method for wind turbines,with application to the monitoring of the gearbox

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition‐based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA‐based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post‐processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.     

Management of fluctuations in wind power and CHP comparing two possible Danish strategies

Both CHP (combined heat and power production) and wind power are important elements of Danish energy policy. Today, approximately 50% of both the Danish electricity and heat demand are produced in CHP and more than 15% of the electricity demand is produced by wind turbines. Both technologies are essential for the implementation of Danish climate change response objectives, and both technologies are intended for further expansion in the coming decade. Meanwhile, the integration of CHP and wind power is subject to fluctuations in electricity production. Wind turbines depend on the wind, and CHP depends on the heat demand. This article discusses and analyses two different national strategies for solving this problem. One strategy, which is the current official government policy known as the export strategy, proposes to take advantage of the Nordic and European markets for selling and buying electricity. In this case, surplus electricity from wind power and CHP simply will be sold to neighbouring countries. Another strategy, the self-supply strategy, runs the CHP units to meet both demand and the fluctuations in the wind scheduling. In this case, investments in heat storages are necessary and heat pumps have to be added to the CHP units. Based on official Danish energy policy and energy plans, this article quantifies the problem for the year 2015 in terms of the amount of surplus electricity, and investments in heat pumps, etc. needed to solve the problem are calculated. Based on these results between the two different strategies, the conclusion is that the self-supply strategy is recommended over the official export strategy.     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

A quasi-one-dimensional CFD model for multistage turbomachines

The objective of this paper is to present a fast and reliable CFD model that is able to simulate stationary and transient operations of multistage compressors and turbines. This analysis tool is based on an adapted version of the Euler equations solved by a time-marching, finite-volume method. The Euler equations have been extended by including source terms expressing the blade-flow interactions. These source terms are determined using the ve- locity triangles and a row-by-row representation of the blading at mid-span. The losses and deviations undergone by the fluid across each blade row are supplied by correlations. The resulting flow solver is a performance pre- diction tool based only on the machine geometry, offering the possibility of exploring the entire characteristic map of a multistage compressor or turbine. Its efficiency in terms of CPU time makes it possible to couple it to an optimization algorithm or to a gas turbine performance tool. Different test-cases are presented for which the calculated characteristic maps are compared to experimental ones.     

Optimal economic-emission performance of fuel cell/CHP/storage based microgrid

In this paper, optimal heat and power dispatch of the fuel cell (FC) and combined heat and power (CHP) based microgrid (MG) in grid-connected mode is studied in the presence of demand response program (DRP). Considering cost and emission minimization has turned this study to a multi-objective problem. Multiple generating and storing units such as FC, CHP, power-only unit, boiler, battery storage system, and heat buffer tank are considered in investigated MG. Also, demand response program has been modeled, and the effects of such programs on the load profile have been discussed. The DRP transfers some amount of load from peak periods to other periods which flats the load curve and minimizes total cost and emission of the MG. To solve the multi-objective optimization problem, the Pareto solutions are generated by using the compromising programming, then, optimal solution is chosen by implementing the fuzzy satisfying approach. In comparison with other methods, the proposed method has reduced the set of efficient solutions to a more reasonable size without demanding any information about the decision making parameters. Finally, the problem is solved in two cases as with and without DRP to clarify the impact of DRP on MG scheduling.     

A MINLP model including the pressure levels and multiperiods for CHP process optimisation

In this paper a mixed integer nonlinear programming (MINLP) model for small-scale combined heat and power (CHP) plant synthesis and operation is presented. The model includes also the modelling of pressures levels and part load operation unlike the model presented in earlier work. Also, a detailed model of a back-pressure steam turbine is added to the model, taking into account the efficiency changes in turbine stages and the steam extraction pressure dependence on the steam mass flow rate. The sensitivity analysis shows that the developed model can be solved fairly reliably with commercial local MINLP solvers. The model corresponds well with a simulation model based on an existing plant. The model is able to find improved small-scale CHP designs that have higher efficiencies and that are profitable for wide ranges of electricity prices and fossil CO₂ emission permit prices. The developed model is a suitable decision making tool when evaluating the trade-offs between investments, profits, and fossil CO₂ emissions in small-scale CHP plant synthesis and operation. However, the formulations of the model are not limited to small-scale CHP processes and can be used to model also larger CHP processes and energy systems.     

Cogeneration plant in a pasta factory: Energy saving and environmental benefit

Italy produces approximately 4,520,000 tons of pasta annually, which is about 67% of its total productive potential. As factories need electric and thermal energy simultaneously, combined heat and power (CHP) systems are the most suitable. This paper describes a feasibility study of a CHP plant in a pasta factory in Italy while analyzing energy saving and environmental benefits. Commercially available CHP systems suitable for the power range of energy demand in pasta production use reciprocating engines or gas turbines. This study demonstrates how their use can reduce both energy costs and CO₂ equivalent greenhouse gas emission in the environment. An economic analysis was performed following the methodology set out by Italian National Agency for Technology, Energy and Environment (ENEA) based on a discounted cash flow (DCF) method called “Valore Attuale Netto” (VAN), which uses a cash flow based on the saving of energy when using different energy processes.     

Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm

In this paper, cuckoo optimization algorithm is implemented to solve energy production cost minimization in a combined heat and power (CHP) generation system. This problem is also known as combined heat and power economic dispatch problem, which looks for optimal values of power and heat generation of each CHP unit to minimize the total production cost. Cuckoo optimization algorithm is a new metaheuristic algorithm. It is inspired by the life of a bird family, called cuckoo, that special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this algorithm. Unlike of the some previous approaches, the effect of valve point is considered in the cost function and clearly formulated in the conventional polynomial cost function as absolute sinusoidal term. The proposed method is applied to three small (with three different test cases), medium, and large test systems in order to evaluate its efficiency and feasibility. The obtained results demonstrated a higher quality solution and superior performance of the proposed cuckoo optimization algorithm method in comparison with many existing methodologies.     

Best economical hybrid energy solution: Model development and case study of a WDS in Portugal

This work aims at the development of an integrated model for analysis and optimization of operating strategies of Water Distribution Systems (WDS), taking into consideration economical-hydraulic-power performances. For this purpose, a software tool has been developed based on the following procedures: (i) an Artificial Neural Network (ANN) to determine the best economical hybrid energy system; (ii) for the ANN training process an energy Configuration type and Economical base Simulator model (CES) is used; (iii) as well a Hydraulic and Power Simulator model (HPS) to describe the hydraulic system behaviour; (iv) a performance assessment tool based on an optimization module to minimize pumping costs and maximize the hydraulic reliability and energy efficiency is then implemented. The Artificial Neural Network uses scenarios with only grid supply, grid combined with hydro-turbine, or with wind turbine and a mutual solution, with hydro and wind turbine. The results obtained show how the model is useful for decision support solutions in the planning of sustainable hybrid energy solutions that can be applied to water distribution systems or others existent hydro-systems, allowing the improvement of the global energy efficiency. A real case study of a small WDS in Portugal is analyzed.     

A review on energy,economical, and environmental benefits of the use of CHP systems for small commercial buildings for the North American climate

The use of combined heating and power (CHP) systems to produce both electricity and heat is increasing rapidly due to their high potential of reducing primary energy consumption (PEC), cost, and emissions in domestic, commercial, and industrial applications. In addition to producing both electricity and heat, CHP systems can be coupled with vapor compression systems to provide cooling. This paper analyzes a natural gas engine CHP system together with a vapor compression system for different American climate zones. Performance is measured in terms of operational costs, PEC, and carbon dioxide emissions as a percent of a reference building. The objective of this paper is to compare the performance of a CHP system operating 24 h a day with a system that only operates during typical office hours. Furthermore, the system is optimized based on reducing PEC, minimizing costs, and reducing emissions. In addition, the benefits of CHP systems based on the Energy Star program and the Leadership in Energy and Environmental Design (LEED) program are presented. Results show that, in general, it is more beneficial to operate the CHP system during typical office hours than to operate the system 24 h a day. Also, the CHP system performance strongly depends on the location where it is installed. In addition to reductions in cost, primary energy, and emissions, CHP systems can help achieve the Energy Star label for commercial office buildings and help obtain LEED points that go toward achieving LEED certification status. Copyright © 2009 John Wiley & Sons, Ltd.     

Gas turbines in district heating combined heat and power systems: influence of performance on heating costs and emissions

Much work is currently focussed on identifying economically and environmentally optimal strategies for increasing gas turbine based combined heat and power (CHP). In many such studies, only a few fixed parameters are used to describe the CHP plant. These are typically total and electrical efficiencies, investment and running costs, minimum and maximum acceptable size, and minimum acceptable part-load. However, for gas turbine based systems these characteristics are clearly functions of the operating conditions, especially for part-load operation. This study examines the effects of varying performance of the gas turbine on the overall heat production costs and CO₂ emissions of a medium sized community district heating plant. Both single and double-shaft engines are considered in the study. The results show that the assumption of constant efficiencies for all operating conditions leads to an overestimation of the optimal CHP plant size, thereby underestimating the heat production costs and overestimating the CO₂ emissions of the plant. The results also show marked differences according to the type of gas turbine used and part-load operating strategy adopted. In particular, the paper discusses the part-load operating difficulties for CHP plants running gas turbines equipped with low emissions burners.     

Effective biomass integration into existing combustion plant

Fossil fuels such as coal still dominate in current energy production plants. However, due to their large carbon footprint caused by combustion, rising prices and the unclear an increased interest in renewable and alternative fuels is observable. By 2020, renewable energy should account for 20% of the EU’s final energy consumption in order to reduce the negative impacts of the utilization of fossil fuels. Biomass-based fuels contribute to this effort.The optimization approach introduced in this article supports sustainable and financially feasible biomass integration into the existing large energy producing system with combined heat and power (CHP) production. The objective is to identify optimal conditions (optimal amount of burned fuels with respect to energy demands and energy flows through key components) with regard to maximum annual financial profit.The general mathematical model of a CHP plant utilizing more types of fuels is introduced and an optimization problem is formulated. The approach application is demonstrated on a case study involving existing CHP plant co-firing coal and biomass. The optimization problem is implemented and solved in GAMS (General Algebraic Modeling System). A sensitivity analysis of crucial parameters is performed and the results are presented and discussed.     

Cuckoo search for wind farm optimization with auxiliary infrastructure

This paper focuses on the optimization problem of a wind farm layout. This area of research is currently receiving widespread attention, as optimal positioning of the turbines promotes the financial viability of the wind farm and enhances the competitiveness of wind projects in the energy market. In this work, cuckoo search (CS), a modern population‐based metaheuristic optimization algorithm, is used. The objective is to find the turbine layout and types that maximize the net present value of the wind farm, while constraints on the turbine positions have to be met. The following constraints are considered: Firstly, the minimum distance between turbines for safe operation; secondly, a realistic wind farm shape including forbidden zones for installation and the existing infrastructure. Furthermore, the optimization of the wind farm includes an algorithm to find the least expensive layout of the wind farm roads and the electrical collector system. The algorithm is based on Dijkstra's shortest path and Prim's minimum spanning tree algorithms. The test results indicate that the infrastructure cost has a significant effect on the optimum wind farm solution. A genetic algorithm, commonly applied to wind farm micro‐siting problems, is used to benchmark the performance of the CS. The results show that the CS is capable of consistently finding better solutions than the genetic algorithm.