Thermoeconomic modeling of micro-CHP (micro-cooling,heating, and power) for small commercial applications

The increasing demand for electrical power as well as energy for heating and cooling of residences and small commercial buildings is a growing worldwide concern. Micro-cooling, heating, and power (micro-CHP), typically designated as less than 30 kW electric, is decentralized electricity generation coupled with thermally activated components for residential and small commercial applications. The number of combinations of components and parameters in a micro-CHP system is too many to be designed through experimental work alone. Therefore, theoretical models for different micro-CHP components and complete micro-CHP systems are needed to facilitate the design of these systems and to study their performance. This paper presents a model for micro-CHP systems for residential and small commercial applications. Some of the results that can be obtained using the developed model include the cost per month of operation of using micro-CHP versus conventional technologies, the amount of fuel per month required to run micro-CHP systems, the overall efficiency of micro-CHP systems, etc. A case study is used to demonstrate differences in the system performances of micro-CHP systems driven by a natural gas internal combustion engine and a diesel engine. Some of the results show that both systems have similar performance and that system total efficiencies in cooler months of up to 80% could be obtained. Also, modeling results show that there is a limit in fuel price that economically prevents the use of CHP systems, which is $11 MBTU⁻¹ for this specific case. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal Design of Distributed Energy Resource Systems under Uncertainties Based on Two-Stage Robust Optimization

Distributed energy resource(DER)systems are widely used owing to their excellent economic and environmental performance.However,uncertainties in the system generate difficulties in the optimal design of DER systems.In practice,the distribution of uncertain parameters is generally unknown.In this work,a two-stage robust optimization(RO)model was proposed for the optimal design of DER systems considering uncertainties in renewable energy intensity,energy prices,and load demands.Three uncertainty sets(i.e.,the box,ellipsoid,and convex-hull uncertainty sets)were adopted to describe the distribution of uncertain parameters,and the proposed two-stage RO problem was solved using affine decision rules.A typical hospital in Lianyungang,Jiangsu Province,China,was selected as the case study object,and the effectiveness of the model was verified.The case study results showed that uncertainties in energy prices and load demands have a significant impact on system configuration and economic performance,and mainly affect the installed capacities of gas boilers,absorption chillers,and storages.Uncertainty set will affect the optimization results and an appropriate uncertainty set should be adopted to describe uncertainties precisely and increase accuracy of results.     

The financial viability of an SOFC cogeneration system in single-family dwellings

In the near future, fuel cell-based residential micro-CHP systems will compete with traditional methods of energy supply. A micro-CHP system may be considered viable if its incremental capital cost compared to its competitors equals to cumulated savings during a given period of time. A simplified model is developed in this study to estimate the operation of a residential solid oxide fuel cell (SOFC) system. A comparative assessment of the SOFC system vis-à-vis heating systems based on gas, oil and electricity is conducted using the simplified model for a single-family house located in Ottawa and Vancouver. The energy consumption of the house is estimated using the HOT2000 building simulation program. A financial analysis is carried out to evaluate the sensitivity of the maximum allowable capital cost with respect to system sizing, acceptable payback period, energy price and the electricity buyback strategy of an energy utility. Based on the financial analysis, small (1–2 kW_e) SOFC systems seem to be feasible in the considered case. The present study shows also that an SOFC system is especially an alternative to heating systems based on oil and electrical furnaces.     

Solid oxide fuel cell micro combined heat and power system operating strategy: Options for provision of residential space and water heating

Solid oxide fuel cell (SOFC) based micro combined heat and power (micro-CHP) systems exhibit fundamentally different characteristics from other common micro-CHP technologies. Of particular relevance to this article is that they have a low heat-to-power ratio and may benefit from avoidance of thermal cycling. Existing patterns of residential heat demand in the UK, often characterised by morning and evening heating periods, do not necessarily complement the characteristics of SOFC based micro-CHP in an economic and technical sense because of difficulties in responding to large rapid heat demands (low heat-to-power ratio) and preference for continuous operation (avoidance of thermal cycling). In order to investigate modes of heat delivery that complement SOFC based micro-CHP a number of different heat demand profiles for a typical UK residential dwelling are considered along with a detailed model of SOFC based micro-CHP technical characteristics. Economic and environmental outcomes are modelled for each heat demand profile. A thermal energy store is then added to the analysis and comment is made on changes in economic and environmental parameters, and on the constraints of this option. We find that SOFC-based micro-CHP is best suited to slow space heating demands, where the heating system is on constantly during virtually all of the winter period. Thermal energy storage is less useful where heat demands are slow, but is better suited to cases where decoupling of heat demand and heat supply can result in efficiencies.     

More accurate sizing of renewable energy sources under high levels of electric vehicle integration

Electric vehicles (EVs) and distributed generation are expected to play a major role in modern power systems. Although many studies have introduced novel models to integrate distributed generation into high levels of EV-adoption scenarios, none has considered EV-embedded battery performance degradation and its economic effect on system planning. Based on well-established models and data to emulate the capacity fading of lithium-ion batteries, the current work presents a mixed-integer linear programming optimization framework with decision variables to size renewable energy resources (RESs) in modern microgrids. The objective function aims to minimize the total cost of the system while guaranteeing a profitable operation level of vehicle-to-grid (V2G) application, narrowing the gap between design stage and real-life daily operation patterns. Stochastic modeling is used to incorporate the effect of different uncertainties involved in the issue. A case study on a residential system in Okinawa, Japan, is introduced to quantitatively illustrate how a profitable V2G operation can affect RES sizing. The results reveal that accounting for the economic operation of EVs leads to the integration of significantly higher capacities of RESs compared with a sizing model that excessively relies on V2G and does not recognize battery-fading economics.     

On policy instruments for support of micro combined heat and power

The performance of residential micro combined heat and power (micro-CHP)—a technology to provide heat and some electricity to individual dwellings—is generally dependent on the magnitude of household thermal energy demand. Dwellings with larger and more consistent thermal consumption perform well economically and achieve greater greenhouse gas emissions savings. Consequently, the performance of micro-CHP is dependent on the level of thermal insulation in a dwelling. Therefore, emerging policy approaches regarding energy use in the residential sector, which generally support both energy efficiency measures such as thermal insulation and adoption of micro-CHP, may inadvertently incentivise micro-CHP installation where CO₂ reductions are meagre or not cost-effective. This article examines this issue in terms of the changes in economic and environmental performance that occur for three micro-CHP technologies under changing patterns of residential thermal insulation in the United Kingdom. The results of this analysis are used to comment on the structure of policy instruments that support micro-CHP. It is found that simultaneous support for energy efficiency measures and micro-CHP can be justified, but care must be taken to ensure that the heat-to-power ratio and capacity of the micro-CHP system are appropriate for the expected thermal demand of the target dwelling.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

Fuel cell micro-CHP techno-economics: Part 1 – model concept and formulation

This article presents the concept and mathematical treatment for a techno-economic modelling framework designed to enable exploration of fuel cell micro combined heat and power (micro-CHP) system design and control. The aim is to provide a tool that can help to focus research and development attention on the system characteristics critical for commercial success of these technologies, present cost targets for developers, and to ensure policy makers provide appropriate instruments to support commercialisation. The model is distinctive in that it applies mixed integer unit commitment formulation to link design and control decisions for micro-CHP, and explicitly characterises stack degradation in a techno-economic framework. It is structured to provide depiction of the fuel cell stack and balance-of-plant, supplementary thermal-only system (e.g. tail gas burner), thermal energy storage, and electrical power storage. Technically, the fuel cell stack is characterised by steady-state thermal and electrical efficiencies for full and part-load operation, its nameplate capacity, minimum operating set-point, and stack degradation via performance loss rate proportional to power density and thermal cycling rate. The dynamics of operation are emulated via ramp limits, minimum up-time and minimum down-time constraints, and start-up and shutdown costs and energy consumptions. The primary performance evaluation metric adopted is the maximum additional capital cost a rational investor would pay for the fuel cell micro-CHP system over and above what they would pay for a competing conventional heating system. The companion article (Part 2) applies the developed model to consider the impact of stack degradation on economic and environmental performance.     

Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power

When modelling the environmental and economic aspects of meeting a given heat and power demand with a combination of combined heat and power (CHP) and grid power, it is common to use a coarse temporal precision such as 1-h demand blocks in heat and power demand data. This may be appropriate for larger applications where demand is reasonably smooth, but becomes questionable for applications where demand exhibits substantial volatility such as for a single residential dwelling—an important potential market for the commercialisation of small-scale fuel cells and other micro-CHP. Choice of temporal precision is also influenced by the relative ease in obtaining coarse data, their compatibility with available energy price data, and avoidance of computational overheads when data sets expand. The thesis of this paper is that use of such coarse temporal precision leads to averaging effects that result in misleading environmental and economic outcomes for cost-optimal micro-CHP systems. Much finer temporal precision is required to capture adequately the specific characteristics of residential energy demand and the technical qualities of solid oxide fuel cell and stirling engine micro-CHP systems. This thesis is generally supported by the results of analysis, which shows that in some cases optimal design generation capacity of the CHP system is reduced by more than half between analyses using 1-h precision and 5-min precision energy demand data. When optimal dispatch of given generator and boiler capacities is considered, the quantities of energy delivered by the various components of the energy provision system (i.e. generation from CHP, heat from CHP, heat from an additional boiler, electricity from grid) varied by up to 40% between precisions analysed. Total CO₂ emissions reduction is overestimated by up to 40% by the analyses completed using coarse demand data for a given micro-CHP generator capacity. The economic difference is also significant at up to 8% of lifetime costs for a given micro-CHP generator capacity.     

Planning of micro-combined heat and power systems in the Portuguese scenario

High efficiency cogeneration is seen by the European Commission as part of the solution to increase energy efficiency and improve security of supply in the internal energy markets. Portuguese residential sector has an estimated technical market potential of around 500 MW_e for cogeneration of <150 kW_e in size. Additionally, in Portugal there is a specific law for power production in low voltage, where at least 50% of the produced electric energy must be own consumed and the maximum power delivered to the power utility should be less than 150 kW_e. Therefore, generic application tools cannot be applied in this regard. In this work, we develop the MicroG model for planning micro-CHP plants in agreement with the Portuguese energy legal framework. The model is able to design, evaluate and optimize from the techno-economic point of view any micro-CHP plant. MicroG appeals to some data bases, such as micro-cogeneration technologies and power consumption profiles that are also described. In addition, a practical case on a gym is considered to show all the functionalities of the model. The developed model has proven to be extremely useful from the practical point of view. This model could help the development of the micro-CHP Portuguese market, which in turns contributes to accomplish the targets of Kyoto protocol and EU cogeneration Directive. Other improvements to MicroG model can be made in order to enlarge the range of application to other micro-cogeneration technologies and to accomplish with the CO₂ emissions trading.     

Modeling and off-design performance of a 1 kWe HT-PEMFC (high temperature-proton exchange membrane fuel cell)-based residential micro-CHP (combined-heat-and-power) system for Danish single-family households

A novel proposal for the modeling and operation of a micro-CHP (combined-heat-and-power) residential system based on HT-PEMFC (High Temperature-Proton Exchange Membrane Fuel Cell) technology is described and analyzed to investigate its commercialization prospects. An HT-PEMFC operates at elevated temperatures, as compared to Nafion-based PEMFCs and therefore can be a significant candidate for cogeneration residential systems. The proposed system can provide electric power, hot water, and space heating for a typical Danish single-family household. A complete fuel processing subsystem, with all necessary BOP (balance-of-plant) components, is modeled and coupled to the fuel cell stack subsystem. The micro-CHP system is simulated in LabVIEW™ environment to provide the ability of Data Acquisition of actual components and thereby more realistic design in the future. A part-load study has been conducted to indicate performance characteristics at off-design conditions. The system is sized to provide realistic dimensioning of the actual system.     

Assessment of residential battery storage systems and operation strategies considering battery aging

With the increasing popularity of combining residential photovoltaic systems with battery storages, research, industry, and customers look for ways to determine if such an investment is economically profitable. Simulation programs may serve to predict the profitability and lifetime of the system. In this paper, we use techno-economic analysis with a specific account of battery degradation to determine profitability and lifetime of a residential photovoltaic (PV) battery system under different energy management and tariff regimes. This work presents two case studies: the first being a techno-economic comparison for a residential PV-battery system in New South Wales, Australia and Germany, and the second analyzing the profitability and degradation impact of three different operation strategies for a battery storage in Australia. The results reveal that site-specific conditions (i.e., geographical and energy-economic constraints) may have a significant impact on the ideal system configuration and ultimately the anticipated battery lifetime. Furthermore, statistical analysis of different storage operation strategies applied to various prosumer load and generation profiles reveals the effects of storage dispatch strategies on battery aging.     

Modeling and parametric study of a 1 kWe HT-PEMFC-based residential micro-CHP system

A detailed thermodynamic, kinetic and geometric model of a micro-CHP (Combined-Heat-and-Power) residential system based on High Temperature-Proton Exchange Membrane Fuel Cell (HT-PEMFC) technology is developed, implemented and validated. HT-PEMFC technology is investigated as a possible candidate for fuel cell-based residential micro-CHP systems, since it can operate at higher temperature than Nafion-based fuel cells, and therefore can reach higher cogeneration efficiencies. The proposed system can provide electric power, hot water, and space heating for a typical Danish single-family household. A complete fuel processing subsystem, with all necessary balance-of-plant components, is modeled and coupled to the fuel cell stack subsystem. The micro-CHP system’s synthesis/design and operational pattern is analyzed by means of a parametric study. The parametric study is conducted to determine the most viable system/component design based on maximizing total system efficiency, without violating the requirements of the system. Four decision variables (steam-to-carbon ratio, fuel cell operating temperature, combustor temperature and hydrogen stoichiometry) were parameterized within feasible limits to provide insight on their effect on the overall performance of the proposed system under study and also to provide input on more efficient design in the future. The system is designed to provide maximum loads of 1 kW_e and 2 kW_th. A sensitivity analysis is applied to investigate the influence of the most important parameters on the simulated performance of the system.     

Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran

Renewable distributed generation introduced as an environmental friendly alternative energy supply while it provided the power system with ever-growing technical benefits such as loss reduction and feeder voltage improvement. The evaluation of the effects of small residential photovoltaic and wind DG systems on various system operating indices and the system net load is complicated by both the probabilistic nature of their output and the variety of their spatial allocations. The increasing penetration of renewable distributed generation in power systems necessitates the modeling of this stochastic structure in operation and planning studies. An advanced stochastic modeling of the system requires multivariate uncertainty analysis involving non-normal correlated random variables. Such an analysis is to epitomize the aggregate uncertainty corresponding to spatially spread stochastic variables. In this paper, an integration study of photovoltaics and wind turbines, distributed in a distribution network, is investigated based on the stochastic modeling using Archimedean copulas as a new efficient tool. The basic theory concerning the use of copulas for dependence modeling is presented and focus is given on an Archimedean algorithm. A comprehensive case study for Davarzan area in Iran is presented after reviewing Iran's renewable energy status. This study shows an application of the presented technique when large datasets, assuming 10-min interval between data points of PV, wind and load profiles, are involved where a deterministic study is not trivial.     

Application of an improved operational strategy on a PBI fuel cell-based residential system for Danish single-family households

A proposed residential energy system based on the PBI (Polybenzimidazole) fuel cell technology is analyzed in terms of operational performance. Conventional operational strategies, such as heat-led and electricity-led, are applied to the simulated system to investigate their performance characteristics. Based on these findings, an improved operational strategy is formulated and applied in an attempt to minimize the shortcomings of conventional strategies. System parameters, such as electrical and thermal efficiencies, heat dumping, and import/export of electricity, are analyzed. The applied load profile is based on average data for a single-family household in Denmark and includes consumption data for electricity and heat demands. The study analyzes the potential of the proposed system on market penetration in the area of residential heat-and-power generation and whether this deployment can be justified as compared to other micro-CHP system technologies. The most important findings of this research study indicate that in comparison to non-fuel cell-based micro-CHP systems, such as Stirling Engine-based systems, the proposed system has significantly higher efficiencies. Moreover, the lower heat-to-power ratios allow the system to avoid high thermal surpluses throughout the whole annual operational profile.     

Micro-combined heat and power in residential and light commercial applications

The cogeneration is worldwide considered as the major option to achieve considerable energy saving with respect to traditional systems. This paper deals with the application of micro-cogeneration (electrical power <15 kW) to small scale (residential and light commercial application) users; the state of art of this technology is considered and an energetic analysis of MCHP system regarding its utilization in conjuction with domestic household appliances is performed. Finally the test facility designed and built to evaluate the performance of micro-CHP system itself is described and the optimum operation mode to match the user’s thermal and electrical loads identified.     

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Development of an interval multi‐stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty

A regional energy system consists of diverse forms of energy. Energy‐related issues such as utilization of renewable energy and reduction of greenhouse gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi‐stage stochastic programming regional energy systems planning model (IMSP‐REM) was developed to support regional energy systems management and GHG control under uncertainty. The IMSP‐REM is a hybrid methodology of inexact optimization and multi‐stage stochastic programming. Not only can it handle uncertainties presented as intervals and probability density functions but also reflect dynamics of system conditions over multiple planning stages. The developed IMSP‐REM was applied to a hypothetical regional energy system. The results indicate that the IMSP‐REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy system planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. Copyright © 2011 John Wiley & Sons, Ltd.     

A method for remaining discharge time prediction of lithium‐ion batteries under dynamic uncertainty

Remaining discharge time of the battery system in electric vehicles relates strongly to the decision‐making of driving. Subjected to the various uncertainties, such as modeling uncertainty, state estimation uncertainty, and future load uncertainty, the accuracy and reliability of the remaining discharge time prediction reduce, which will lead to range anxiety. A stochastic framework based on the state‐of‐charge estimation and prediction strategy is proposed to predict remaining discharge time against the uncertainty. Firstly, the equivalent circuit model is established to model the dynamic behavior of the battery. Through the analysis of different fitting functions of open circuit voltage and state‐of‐charge, the Akaike information criterion is introduced to select the best function. Secondly, the coestimator is employed to bound the influence of the model parameter uncertainty and noise uncertainty. Finally, the uncertainties are quantified by a probabilistic method, and a Monte Carlo–based stochastic prediction strategy is proposed to predict the probability distribution of remaining discharge time under dynamic uncertainty. Experimental results demonstrate that the proposed prediction framework is of great effectiveness as it can provide an accurate remaining discharge time interval under dynamic uncertainty, which helps to promote the energy‐saving driving and overcome the range anxiety.     

Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach

In this study, an inexact community-scale energy model (ICS-EM) has been developed for planning renewable energy management (REM) systems under uncertainty. This method is based on an integration of the existing interval linear programming (ILP), chance-constrained programming (CCP) and mixed integer linear programming (MILP) techniques. ICS-EM allows uncertainties presented as both probability distributions and interval values to be incorporated within a general optimization framework. It can also facilitate capacity-expansion planning for energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term renewable energy management planning for three communities. Useful solutions for the planning of energy management systems have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The generated solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and energy structure, and (c) analysis of interactions among economic cost, system reliability and energy-supply security.